DETROIT -- Ford Motor Co. is rolling out a new U.S. dealership training platform using artificial intelligence that it hopes will improve retailers' product knowledge and customer relationships.

The new system, called Ford University, was developed over the past year and features instructional videos, focused on what the company calls "cinematic storytelling," that explain topics from Ford history to technical breakdowns of new vehicle features. Dealership staff will be asked to complete "missions," or videos of themselves simulating interactions with customers, which will be graded through AI analysis.

The system will be able to tell if a salesperson's intonations are too joyful or angry, if they say "um" or "like" too much and whether they're hitting enough key phrases or facts in describing a Ford vehicle compared with rival brands. Should a user receive a failing score, the AI will suggest specific ways to improve and offer opportunities to try again.

The platform, powered by GPT and Microsoft Copilot, goes live Wednesday for Ford's roughly 3,000 U.S. dealerships. Initial content will be geared toward nontechnical dealership employees, with plans to add technical employee content by the end of the year.

"It is very much an opportunity for us to completely reimagine how our dealership training is done today," said Abby Vietor, Ford's global director of dealer training and productivity. "It's very much meant to be a completely different type of experience, but one where all the activities are geared toward reinforcement of knowledge and really helping the dealership employees translate that knowledge into a customer conversation."

The rollout comes as Ford works to improve its communication with its dealer body while it shifts the way some of its vehicles are sold. Vietor said company officials viewed the need to provide better dealership training as a top priority.

While the company will have access to data on how each dealership employee performs, Vietor said Ford would not use it to punish stores with low scores -- or reward those with high marks -- but would use the insights to tweak which tools and content it sends out. Dealers only have access to data from their specific stores, but Vietor said in the future, a store could possibly see how its scores rank among peers in a specific region or group, if all dealers opt into sharing that information.

Retailers who have tested the platform say they appreciate how interactive it is.

"What I really like was it gave some good feedback," Peter Battle, general manager of the customer service division at Pat Milliken Ford in Redford, Mich., told reporters at a briefing in Dearborn, Mich. "It didn't 'pass/fail' me; there was some personal coaching involved. It much more fits today's society and how people learn."

Battle said the way Ford University is set up makes it easier to retain information and receive personalized tips.

"I love coaching within the store, but I can't do it with everybody," Battle said. "This gives you the ability to do that."

Vietor declined to say how much Ford spent on the platform. She said the company designed it to feel like the channels that people typically use to watch videos, such as YouTube or Netflix.

"We wanted to have it look like something they're already comfortable with and interacting with," Vietor said.

She said there will be a gamification element to the platform to encourage users to view more content, similar to how Starbucks gives customers more rewards points for buying specific items on certain days through its smartphone app.

Bill Hasler, the executive producer of Ford University, said the company has dozens of content creators who can quickly make or update videos. He hopes dealership employees will want to watch and explore the platform even during their free time.

"I want to get into competition with Netflix and Amazon," he said. "If our dealers go home at the end of the day and they're watching TikTok or YouTube, or they're going to watch a rerun of 'Seinfeld,' someday I want them to pick up the Ford University app and maybe watch a 3-minute video on what we did."

Automotive News

Ford launches AI-powered dealership training platform

Researchers at Amazon Web Services have come up with new ways to scrub bad data from an AI model that can lead to bias, data privacy violations, or copyright infringement.

The idea, if it works, would be a major development in allowing companies to revise models once they've been trained. It could also help them better comply with rules to protect private information or intellectual property, like Europe's General Data Protection Regulation, which includes the right to be forgotten.

Neural networks, like generative AI models, are trained to perform specific tasks by learning complex patterns from data. In some cases, however, developers may want to remove some data from the model if it exhibits incorrect or harmful behaviors. A company might also want to block AI from copying artists' work, disclosing sensitive documents, or generating false information for example. But it's difficult to remove these deleterious effects; they either have to take the model down or retrain it from scratch on better data, which is expensive.

"Even with the most careful controls, when you are training models with trillions of pieces of data, there could be mistakes. So we need to be able to plan ahead to know what to do when these mistakes are revealed," Stefano Soatto, vice president of AWS AI Labs and a computer science professor at the University of California, Los Angeles, told Semafor in an interview. "Right now, the solution is to throw everything away and learn from scratch, which is quite costly and impacts energy and the environment. It's not just a matter of masking the result, we have to remove or disgorge the information from the train models."

Dubbed "model disgorgement," AWS researchers have been experimenting with different computational methods to try and remove data that might lead to bias, toxicity, data privacy, or copyright infringement. They outlined different techniques in a paper published in the Proceedings of the National Academy of Sciences last month, including splitting the training data into "shards" so it's easier to delete a specific chunk or use synthetic data.

These methods have yet to be applied internally to any commercial models. Soatto said it's "still early days" but may eventually be a feasible solution to fix issues after they've been deployed in the real world.

semafor.com

'Disgorgement': Amazon researchers suggest ways to get rid of bad AI data

Gen AI

Google Research and Google's AI research lab, DeepMind, have detailed the impressive reach of Med-Gemini, a family of advanced AI models specialized in medicine. It's a huge advancement in clinical diagnostics with massive real-world potential.

Doctors treat a multitude of patients daily, with needs ranging from simple to very complex. To deliver effective care, they must be familiar with each patient's health record and keep up-to-date with the newest procedures and treatments. And then there's the all-important doctor-patient relationship, built on empathy, trust, and communication. For an AI to come close to emulating a real-world doctor, it needs to be able to do all of these things.

The intersection of AI and medicine has really taken off. In the last six months, New Atlas has reported on AI models that aid less experienced doctors in identifying the precursors of colon cancer, diagnose childhood autism from eye images, and predict in real-time whether a surgeon has removed all cancerous tissue during breast cancer surgery. But Med-Gemini is something else.

Google's Gemini models are a new generation of multimodal AI models, meaning that they can process information from different modalities, including text, images, videos, and audio. The models are adept at language and conversation, understanding the diverse information they're trained on, and what's called 'long-context reasoning,' or reasoning from large amounts of data such as hours of video or tens of hours of audio.

Med-Gemini has all of the advantages of the foundational Gemini models but has fine-tuned them. The researchers tested these medicine-focused tweaks and included their results in the paper. There's a lot in the 58-page paper; we've selected the most impressive bits.

Arriving at a diagnosis and formulating a treatment plan requires doctors to combine their own medical knowledge with a raft of other relevant information: patient symptoms, medical, surgical and social history, lab results and the results of other investigative tests, and the patient's response to prior treatment. Treatments are a 'movable feast,' with existing ones being updated and new ones being introduced. All these things influence a doctor's clinical reasoning.

That's why, with Med-Gemini, Google included access to web-based searching to enable more advanced clinical reasoning. Like many medicine-focused large language models (LLMs), Med-Gemini was trained on MedQA, multiple-choice questions representative of US Medical License Exam (USMLE) questions designed to test medical knowledge and reasoning across diverse scenarios.

However, Google also developed two novel datasets for their model. The first, MedQA-R (Reasoning), extends MedQA with synthetically generated reasoning explanations called 'Chain-of-Thoughts' (CoTs). The second, MedQA-RS (Reasoning and Search), provides the model with instructions to use web search results as additional context to improve answer accuracy. If a medical question leads to an uncertain answer, the model is prompted to undertake a web search to obtain further information to resolve the uncertainty.

Med-Gemini was tested on 14 medical benchmarks and established a new state-of-the-art (SoTA) performance on 10, surpassing the GPT-4 model family on every benchmark where a comparison could be made. On the MedQA (USMLE) benchmark, Med-Gemini achieved 91.1% accuracy using its uncertainty-guided search strategy, outperforming Google's previous medical LLM, Med-PaLM 2, by 4.5%.

On seven multimodal benchmarks, including the New England Journal of Medicine (NEJM) image challenge (images of challenging clinical cases from which a diagnosis is made from a list of 10), Med-Gemini performed better than GPT-4 by an average relative margin of 44.5%.

"While the results ... are promising, significant further research is needed," the researchers said. "For example, we haven't considered restricting the search results to more authoritative medical sources, using multimodal search retrieval or performed analysis on accuracy and relevance of search results and the quality of the citations. Further, it remains to be seen if smaller LLMs can also be taught to make use of web search. We leave these explorations to future work."

Electronic health records (EHRs) can be long, but doctors need to be aware of what they contain. To complicate matters, they typically contain textual similarities ("diabetes mellitus" vs. "diabetic nephropathy"), misspellings, acronyms ("Rx" vs. "prescription"), and synonyms ("cerebrovascular accident" vs. "stroke") - things that can pose a challenge to AI.

To test Med-Gemini's ability to understand and reason from long-context medical information, the researchers ran a so-called 'needle-in-a-haystack task' using a large, publicly available database, the Medical Information Mart for Intensive Care or MIMIC-III, containing de-identified health data of patients admitted to intensive care.

The goal was for the model to retrieve the relevant mention of a rare and subtle medical condition, symptom, or procedure (the 'needle') over a large collection of clinical notes in the EHR ('the haystack).

Two hundred examples were curated, and each example consisted of a collection of de-identified EHR notes from 44 ICU patients with long medical histories. They had to have the following criteria:

There were two steps to the needle-in-a-haystack task. First, Med-Gemini had to retrieve all mentions of the specified medical problem from the extensive records. Second, the model had to evaluate the relevance of all mentions, categorize them, and conclude whether the patient had a history of that problem, providing clear reasoning for its decision.

Compared to the SoTA method, Med-Gemini performed well on the needle-in-a-haystack task. It rated 0.77 on precision compared to the SoTA method (0.85) and outdid the SoTA method on recall: 0.76 vs. 0.73.

"Perhaps the most notable aspect of Med-Gemini is the long-context processing capabilities because they open up new performance frontiers and novel, previously infeasible application possibilities for medical AI systems," said the researchers. "This 'needle-in-a-haystack' retrieval task reflects a real-world challenge faced by clinicians and Med-Gemini-M 1.5's performance demonstrates its potential to significantly reduce cognitive load and augment clinicians' capabilities by efficiently extracting and analyzing information from vast amounts of patient data."

For an easy-to-understand discussion of these key research points, and an update on the mud-slinging between Google and Microsoft, check out AI Explained's video from 13:38 onwards.

In a test of real-world usefulness, Med-Gemini was asked about an itchy skin lump by a patient user. After asking for an image, the model asked appropriate follow-up questions and correctly diagnosed the rare lesion, recommending what the user should do next.

Med-Gemini was also asked to interpret a chest X-ray for a physician while they were waiting for a formal radiologist's report and formulate a plain English version of the report that could be provided to the patient.

"The multimodal conversation capabilities of Med-Gemini-M 1.5 are promising given they are attained without any specific medical dialogue fine-tuning," the researchers said. "Such capabilities allow for seamless and natural interactions between people, clinicians, and AI systems."

However, the researchers recognize that further work is needed.

"This capability has significant potential for helpful real-world applications, including assisting clinicians and patients, but of course also entails highly significant risks," they said. "While highlighting the potential for future research in this domain, we have not rigorously benchmarked capabilities for clinical conversation in this work as previously explored by others in dedicated research towards conversational diagnostic AI."

Where to from here? The researchers admit that there is much more work to be done, but the Med-Gemini model's initial capabilities are certainly promising. Importantly, they plan to incorporate responsible AI principles, including privacy and fairness, throughout the model development process.

"Privacy considerations in particular need to be rooted in existing healthcare policies and regulations governing and safeguarding patient information," the researchers said. "Fairness is another area that may require attention, as there is a risk that AI systems in healthcare may unintentionally reflect or amplify historical biases and inequities, potentially leading to disparate model performance and harmful outcomes for marginalized groups."

But, ultimately, Med-Gemini is seen as a tool for good.

"Large multimodal language models are ushering in a new era of possibilities for health and medicine," the researchers said. "The capabilities demonstrated by Gemini and Med-Gemini suggest a significant leap forward in the depth and breadth of opportunities to accelerate biomedical discoveries and assist in healthcare delivery and experiences. However, it is paramount that advancements in model capabilities are accompanied by meticulous attention to the reliability and safety of these systems. By prioritizing both aspects, we can responsibly envision a future where the capabilities of AI systems are meaningful and safe accelerators of both scientific progress and care in medicine."

New Atlas

Google's medical AI destroys GPT's benchmark and outperforms doctors

Artificial intelligence is already a huge part of our lives. But why has it not been as well received in the world of entertainment?

Summary

The controversy surrounding AI in filmmaking is increasing, leading to backlash from audiences who feel deceived by AI-generated content. As technology advances, the blurred line between AI-generated and human-created content raises ethical concerns in the film industry. Despite the convenience and cost-saving benefits AI offers, filmmakers are finding that audiences are not accepting of AI-generated elements in movies.

The leaps in technological resources in Hollywood are typically well-received. Industrial Light & Magic revolutionized the industry with their methods to accomplish state-of-the-art special effects. When visual effects were advanced enough, they even got a special category, which is still active at the Academy Awards. Technology isn't usually seen as a threat to auteurs and their craftsmanship, but there's still a place for discussion regarding films that feel like "too much filling but no substance." There's a limit, and when some films cross it, which has happened in 2024, the reception isn't very positive.

Nevertheless, the controversy surrounding AI (artificial intelligence and all technology using it) is on another level. When ChatGPT stood at the center of the controversy a couple of years ago, little did we know cheating on tests was the least of our problems. The technology went ahead and installed itself on just about every medium. Today, there's art made by AI, and wherever you stand on this subject, there's a guarantee that the use of AI makes people talk.

From an artistic point of view, the clearest example of the controversy surrounding AI came out in the open when studios toyed with the idea of having machines write stories that they would then adapt into films and TV shows. Regardless of the effectiveness of such stories, this approach terrified everyone, both in the industry and outside, and this became an important issue during the SAG-AFTRA strike that shook the entire world recently. Writers were not going to take it.

But in recent days, we've seen some examples of AI still being used as a filmmaking component. From altering important images to helping in the marketing department, AI has been used as an enhancer in recent films and TV shows that have been met with extreme backlash. Let's look at why, despite the perceived benefits of AI, it's one of the most controversial subjects in today's industry of films and television.

AI Is Far From Popular, So Why Do Filmmakers Keep Using It?

Late Night With the Devil R 4 /5

Release Date March 22, 2024 Director Cameron Cairnes , Colin Cairnes Cast David Dastmalchian , Laura Gordon , Ian Bliss , Fayssal Bazzi , Ingrid Torelli , Rhys Auteri , Josh Quong Tart , Georgina Haig Main Genre Horror

The mere idea of AI completely replacing part of the crew behind the making of a movie has been met with absolute rejection. Filmmakers who support it don't have enough arguments (aside from the obvious "let's make a movie with fewer people"), and audiences (for the most part) are still able to recognize whenever AI is being used.

One great example of this controversy is the recent horror film Late Night With the Devil. Projected as a potentially scary indie horror film, in which many companies participated, the movie was a possession film that utilized '70s nostalgia to present a stylishly accurate movie. In the movie, all hell breaks loose, and footage from the cameras captures the moment late-night show host Jack Delroy witnesses the wrath of a demon. Starring David Dastmalchian, the film seemed to intrigue audiences. However, the revelation of AI-generated content in the movie came as a shock and may have led to some potential viewers skipping the film.

Related The 10 Greatest Special and Visual Effects Sequences in James Cameron's Movies James Cameron's ability for visual and special effects remains the best in the film industry. These are the best effects sequences in his catalog.

Regardless, when audiences found out the movie contained AI art, they were furious enough to boycott the film altogether, according to Yahoo. The question remains of how much money a production could save by using AI or whether the filmmakers truly believed the "AI aesthetic" fit with the film's tone.

Every "mistake" should work as a cautionary experience for everyone. In this case, there was a lesson to be learned, and unfortunately, it cost a very good film the appreciation it deserves. Hopefully, someone learned a lesson, though it seems entirely avoiding AI in the future of Hollywood may be near impossible.

Movie Budgets Continue to Rise

Civil War R 3.5 /5 Release Date April 12, 2024 Director Alex Garland Cast Jesse Plemons , Nick Offerman , Kirsten Dunst , Cailee Spaeny , Wagner Moura Main Genre Action

AI will continue to be controversial in all facets of life as companies look to replace workers with the tool to cut costs wherever possible as film budgets continue to rise, with many blockbusters reaching over $200 million just for production. Using the newly developed technology, movie studios can eliminate the need for additional artists in the crew and cut down the time needed to create "original" content.

The problem is that a cheaper film doesn't mean it will be as good. However, if cost reduction is essential and AI can substitute the resources you must relinquish, it's likely the best course of action. However, now studios are left with another problem. As seen with Late Night with the Devil, audiences may boycott a film once they see that AI was used in any instance. The best thing they can do is try and hide it, which could lead to even more controversy. In an interview with IndieWire, the directors for Late Night With the Devil said the following:

"In conjunction with our amazing graphics and production design team, all of whom worked tirelessly to give this film the '70s aesthetic we had always imagined, we experimented with AI for three still images, which we edited further and ultimately appear as very brief interstitials in the film."

For indie features, trying to make a film for less money makes sense. But how are big studios and corporations participating in this abhorrent practice? How much do they think will be saved? Sure, a machine won't form a union, ask for a raise, or complain about contract terms, but it also won't deliver an authentic product.

A similar issue has been raised following the release of A24's Civil War. Promotional posters were released as the film hit theaters, and audiences immediately began to accuse A24 of using AI. Coming from A24, a studio that is usually praised by audiences, the move clearly did not go over well.

AI Has Seen Mixed Results

Close

Even if filmmakers and showrunners want to use AI art in films and TV shows to achieve a specific aesthetic, it's still ethically questionable. Not to mention that it often doesn't look good. The interstitials in Late Night with the Devil, while reflecting a '70s aesthetic, could have looked even better with a human creating them. The credits in Disney's Secret Invasion, while somewhat reflecting the themes of the series, still look poor and should never have been approved. The latest, and arguably the worst, controversy revolves around a Netflix documentary film by the name of What Jennifer Did. In the film, viewers find that the main subject appears in a photograph that looks altered. "Messy fingers" is an understatement, yet Netflix still hasn't clarified anything regarding the image.

Related 10 Movies That Hit the Big Screen with Unfinished Special Effects "How could you?!" This is what these movies' filmmakers were shouting when their project was taken from them and released before completion.

This means that technology, as great as it sounds and as much as it advances (and learns) every day, still hasn't arrived at the proper checkpoint. It still can't replicate reality or images created by human hands. The best outcome would be for studios to see the backlash to these various projects and decide that the risk of using AI-generated images and videos is just too high.

AI Seemingly Doesn't Work in Movies and TV Shows. But Will It?

Close

Should we be afraid of AI and the exponential growth of technology? This discussion doesn't provide a definitive answer to this important question. AI won't replace the art filmmakers are able to offer, but perhaps that's not where we should look. The problem seems to lie elsewhere, in consuming content that's heavily manipulated by a machine, with studios hoping audiences won't notice as they cut costs and jobs.

Unfortunately, as technology continues to advance, it may become incredibly difficult to tell the difference between AI-generated images and human ones, even if they can't reach the creative level of a real worker. Indeed, technology seems to be going down that road. AI will, of course, continue to be controversial in films and TV shows, but without mass boycotts, the likelihood of Hollywood studios completely abandoning the tool seems highly unlikely.

Anything that makes your job easier should be well-received. But when it replaces a job entirely, there is cause for concern. Studios may say that artists are still needed to perfect whatever is created by AI. However, there is no telling how long that will last, and some companies will surely cut out the extra employees the first chance they get.

But in the artistic industry, this calls for a deeper analysis. Computers and technology are still obviously tools used by every department involved in making a movie. However, relying on a machine to generate content is unnecessary. Artists can't be replaced by machines for the simple reason that machines can't feel. No matter how much of an emotional landscape you feed into a computing robot, its feedback will be based on calculations. And art shouldn't come from something so... cold.

Movieweb

Why Using AI in Movies and TV Shows Continues to Be So Controversial

It's been over a year since Mark Zuckerberg shared a letter with Meta employees about a "year of efficiency" in which the company would prioritize "flattening our orgs, canceling lower priority projects, and reducing our hiring rates."

That phase is now over for Meta, and Zuckerberg's recent talk on earnings day last month about increased spending on money-losing ventures, like new generative AI models and virtual reality, spooked investors.

But tech companies, by and large, are still closely watching the expense line. IBM has committed to replacing workers with artificial intelligence and is continuing to trim its workforce. With many companies maintaining the "efficiency" mindset, and hiring for tech jobs remaining low, according to the iCIMS Insights April 2024 Workforce Report, innovation is at risk.

"We've seen less innovation on the whole with some of the Covid-era big spenders," said Ricardo Madan, senior vice president of global technology services at TEKsystems. "They're dealing with a whipsaw of that over-hiring that they've had to balance out."

Tech companies have to spend to stay ahead of competitive threats and protect markets. During Meta's earnings discussion, Zuckerberg told analysts, "I think it's worth calling that out, that we've historically seen a lot of volatility in our stock during this phase of our product playbook where we're investing in scaling a new product but aren't yet monetizing it."

Madan says companies tend to act based on fear of missing out, but this behavior can stall technological innovation. A TEKsystems report on the state of digital transformation found that 67% of leaders cite AI and machine learning as the biggest skills gap in their organization. With the amount of time it takes to adequately fill these gaps, Madan argues that maintaining a level head through shifting hiring trends is key.

Chris Duffey, head of strategic development for emerging solutions at Adobe, believes that fully embracing AI can combat this, particularly when used to assess more applicants and accelerate the selection and onboarding processes. Working with vetted strategic partners can help fill these acceleration gaps to streamline both top and bottom-line growth, he says.

Duffey says he lives in a world of innovation, both tangible and speculative, and he says the experts around him are largely in agreement that about a quarter of an IT budget should go towards emerging technologies such as generative AI. This investment will naturally alter the way we do work.

"I used to say proudly that I was a problem solver," said Duffey. "Now with gen AI, we're moving from being problem solvers to problem seekers." Duffey added that generative AI is "taking away those baseline tasks to give us more time to do this higher-level critical thinking and go out and find the problems, the hypotheses, and have gen AI help validate it, or poke some holes into it."

From products that help firms navigate customer service like the Google Cloud contact center AI platform to the future of large action models (LAMs) that personally assist individuals based on their unique data, like those offered by Salesforce, technological innovation is on a strong path. But to keep this going, leaders who choose to tap into workforce efficiency must do so mindfully rather than simply hopping on a bandwagon, Duffy and Madan said.

This process can be messy, which is why experts say any conversation centered around efficiency must also include innovation. "The great leaders that we see," said Madan, "are the ones that really balance that financial and cultural stewardship, and they don't fall prey to the over-speculation and the over-hiring. They balance that with keeping your best and brightest always working on the toughest problems across all the platforms."

Digital transformation is a core pillar of business strategy for 84% of technology and business leaders, according to the TEKsystems report. If the majority of leaders feel this way, it only makes sense their actions align with that. And it's not like the talent isn't available. Despite a competitive market, tech-related jobs continue to be in demand, with applications per opening up 45% year over year, according to the iCIMS report.

The U.S. added more than 300,000 new nonfarm jobs in March, according to the Bureau of Labor Statistics, mostly in healthcare, government and construction. These industries are part of the innovation matrix, too (every company is a tech company, many experts say, and even a range of governmental organizations have joined the trend of hiring a chief AI officer).

"I think we're getting out of that trough of disillusionment," Madan said about hiring trends. On the innovation front, he added, "The best organizations are the ones that synthesize human input with AI collaboration."

CNBC

After Meta's 'year of efficiency,' industry leaders root for year of innovation

La Quinta, California--(Newsfile Corp. - May 1, 2024) - Sycosure, a pioneering SEO agency specializing in online marketing and business solutions, today announces the launch of cutting-edge AI-powered technologies aimed at revolutionizing SEO practices. Since its inception on September 26, 2013, this family-owned small business has been dedicated to providing tailored online marketing solutions. Under the leadership of CEO Alistair Kavalt, Sycosure continues to lead the industry by integrating AI innovations into future SEO strategies.

Sycosure SEO Agency

Artificial intelligence is playing a transformative role in search engine optimization, allowing businesses to enhance their online presence and streamline marketing efforts. By leveraging advanced algorithms and machine learning, AI tools can analyze vast amounts of data, identify trends and patterns, and optimize strategies with unprecedented precision.

Sycosure's new AI-driven tools focus on personalized solutions that cater to the unique needs of each business, aiming to ensure maximum impact and sustainable growth. "At Sycosure, we understand the dynamic nature of online marketing and the necessity of adapting to emerging technologies," says CEO Alistair Kavalt. "Our goal is to empower businesses with AI-driven solutions that not only enhance their online presence but also further their SEO strategies."

Sycosure SEO Agency

Natural language processing and other AI capabilities enable more effective content creation and optimization, ensuring relevance and quality. These tools also help with essential tasks like keyword research and competitor analysis, providing businesses with a competitive edge in the online market.

"As we look to the future, Sycosure remains steadfast in our commitment to innovation and excellence," adds Kavalt. "By partnering with us, businesses can unlock the full potential of AI to navigate the complexities of the digital landscape and potentially seize new opportunities for growth and success."

For more information about Sycosure and its innovative SEO solutions, please visit www.sycosure.com.

About Sycosure:

Sycosure is a family-owned SEO agency based in La Quinta, California. Founded on September 26, 2013, the company is dedicated to providing effective online marketing solutions tailored to the individual needs of each business. With a focus on leveraging cutting-edge AI technologies, Sycosure helps businesses achieve sustainable growth and success in the digital age.

Contact Information:

Company Name: Sycosure

Contact Person: Alistair Kavalt

E-mail: admin@sycosure.com

Phone Number: (909) 584-5141

Source: Story.KISSPR.com

To view the source version of this press release, please visit https://www.newsfilecorp.com/release/207647

SOURCE: Prodigy PR

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Prodigy PR: Sycosure Unveils AI-Powered Innovations, Paving the Way for Future SEO Strategies

The music industry has always been a hotbed of innovation, continuously stretching the limits of creativity and technology. In recent years, generative AI has emerged as a transformative force, offering the potential to revolutionise the production of music.

Earlier this year, we witnessed the launch of Suno, hailed as the 'ChatGPT for music,' utilising generative AI to produce human-like songs instantly from brief text cues. The platform demonstrates the remarkable fusion of AI-generated lyrics, vocals, and music, delivering a complete song experience with just a click.

As AI continues its expansion into entertainment, it's evident that its influence cannot be restrained. Generative AI possessed the power to reshape the musical landscape as we know it, highlighting the symbiotic evolution of technology and musical expression.

With AI-generated platforms successfully creating audiovisual artworks, language learning models, now music, and everything that was once solely associated with human creativity, it is worth our while to stay updated with its latest potential.

"Music is copyrighted with strict laws; however, it is very easy to steal and use it for free," says Alex Mubert, co-founder of a pioneering AI music startup based in the UAE.

"Some AI startups exploit this vulnerability for their benefit and disregard the ethical considerations of the datasets. The same holds true for images, videos, and other creative content. We hope that measures will be implemented on a technical level (such as fingerprints or watermarks) to prohibit such misuse, allowing creators to earn money from AI training on their art."

With a mission to revolutionise music creation, the Russian music producer and entrepreneur introduced a groundbreaking concept back in 2017: real-time generative AI music. This occurred at a time when generative AI wasn't being utilised for music production as it is today.

"When we initially launched Mubert, it marked the inception of one of the first-ever live music generators, a pioneering AI music platform. Prior to our innovation, there were some experiments, such as those conducted by Brian Eno, but none had achieved real-time functionality, nor had any placed musicians at the core of the platform, and made it accessible across iOS, Android, and web interfaces."

The essence of his innovation lies in its fusion of AI technology with human musicianship. Unlike previous experiments in AI-generated music, Mubert argues that his platform places human creativity at its core.

"The reality is, without humans, AI cannot exist. AI learns from datasets created by humans," he adds. "We compensate every musician who contributes data to our platform. Currently, our dataset comprises three million ethically sourced exclusive sounds, with ongoing expansion. We intend to launch a royalty distribution platform to ensure musicians are compensated each time their sounds generate revenue for us."

Central to Mubert's vision is the belief that AI and human creativity are not mutually exclusive but complementary forces. Every sound generated by the platform is sourced from human musicians, ensuring that creativity remains at the forefront.

"When you tap 'Play', it begins to generate music tailored exclusively for you. Each listener enjoys personalised streams. The technology behind it mirrors the creative process of musicians but is fully automated and personalised."

"AI algorithms analyse sounds, generate new ones, arrange them, blend sounds, mix, master, and stream music. Additionally, we gather text prompts, settings, and user interactions to further personalise the experience," he explains.

From producing royalty-free music tailored to specific use cases to personalised music experiences that adapt to individual moods and habits, AI holds immense promise for reshaping the entertainment landscape. However, with great innovation comes ethical considerations.

Mubert acknowledges the copyright implications inherent in AI-generated music and emphasises the importance of respecting artists' intellectual property rights.

"We empower our artists to select the licence for their content, allowing them to later specify the types of companies, apps, and other restrictions for their sounds' usage. This approach grants them the freedom to ethically and transparently monetise their content, ensuring they are compensated each time their music is utilised," he says.

As AI continues to permeate the creative process, he envisions a collaborative future where human ingenuity and machine intelligence converge. "From a product standpoint, AI has long been integrated into music production and will continue to delve deeper into the creative process."

"Artists can start creating tracks using AI and then infuse them with their unique creativity. They can also perform vocals over AI-generated beats and collaborate with renowned artists by utilising AI-driven replicas," he adds.

Amidst the promise of AI-driven creativity, there is a growing concern that it could foster the homogenisation of creative output. Is there a risk that AI might standardise music, resulting in a landscape where all compositions begin to sound alike?

"That's accurate. The diffusion of language models often yields homogeneous outcomes. Nowadays, virtually all content on social media platforms seems to be AI-generated, which eventually makes it all look the same. This phenomenon could extend to music as well," says Mubert.

"This is why our focus lies in exploring generative patterns, resynthesis, and the amalgamation of models to foster the emergence of new genres, song structures, and musical approaches."

For creativity to flourish, artists also require the freedom and opportunity to experiment and push their boundaries without the pressure of always delivering immediate returns on investment (ROI). Can formulaic music generated through AI, which might promise certain ROI, hinder experimentation?

"For sure, it is a possibility and cause for concern. Everyone in the world thinks music is as cheap as $10 per month. Musicians used to be quite rich in the times of vinyl-selling copies. Now they can only earn from royalties (close to zero dollars), selling merch or live gigs," says Mubert. "However, we stand for musicians in this battle and will do our best to protect their best interests."

In the Middle East, Mubert sees both challenges and opportunities in implementing AI-driven solutions for entertainment. While the region's music industry is on the rise, there exists a burgeoning youth culture eager to explore new avenues of expression. Through initiatives like preserving local music heritage and empowering local creators, Mubert aims to catalyse a new era of musical innovation in the region.

"The music industry in the Middle East is still emerging, with a relatively smaller market. However, there is a noticeable trend among the youth to enhance the music culture, presenting ample opportunities for them to pursue careers as musicians. AI is already playing a role in this landscape, offering assistance and innovation," says Mubert.

Slated to speak at the Integrate Middle East Summit 2024 taking place from May 21-23 taking place at the Dubai World Centre, he shares his vision for giving the local music scene an AI boost. "My dream is to preserve local heritage by collecting instruments, melodies, rhythms, beats, and loops, and creating AI-driven public music accessible to everyone," says Mubert.

"This platform would serve as a futuristic hub for preserving heritage and we are constantly searching for niche styles, instruments, and rare melodies to put it all together. I look forward to discussing this further at the summit."

Khaleej times

Ushering in a new music era: How generative AI is being used for song production

The pact teens make with social media feels to them like a good bargain. Picture: PAUL HANAOKA VIA UPSLPASH

Social media apps regularly present teens with algorithmically selected content often described as "for you," suggesting, by implication, that the curated content is not just "for you" but also "about you" -- a mirror reflecting important signals about the person you are.

All users of social media are exposed to these signals, but researchers understand that teens are at an especially malleable stage in the formation of personal identity. Scholars have begun to demonstrate that technology is having generation-shaping effects, not merely in the way it influences cultural outlook, behaviour and privacy, but also in the way it can shape personality among those brought up on social media.

The prevalence of the "for you" message raises important questions about the impact of these algorithms on how teens perceive themselves and see the world, and the subtle erosion of their privacy, which they accept in exchange for this view.

Teens like their algorithmic reflection

Inspired by these questions, my colleagues John Seberger and Afsaneh Razi of Drexel University and I asked: How are teens navigating this algorithmically generated milieu, and how do they recognise themselves in the mirror it presents?

In our qualitative interview study of teens 13-17, we found that personalised algorithmic content does seem to present what teens interpret as a reliable mirror image of themselves, and that they very much like the experience of seeing that social media reflection.

Teens we spoke with say they prefer a social media completely customised for them, depicting what they agree with, what they want to see and, thus, who they are.

It turns out that the teens we interviewed believe social media algorithms such as TikTok's have got so good that they see the reflections of themselves in social media as quite accurate. So much so that teens are quick to attribute content inconsistencies with their self-image as anomalies -- for instance, the result of inadvertent engagement with past content, or just a glitch.

When personalised content is not agreeable or consistent with their self-image, the teens we interviewed say they scroll past it, hoping never to see it again. Even when these perceived anomalies take the form of extreme hypermasculine or "nasty" content, teens do not attribute this to anything about themselves specifically, nor do they claim to look for an explanation in their own behaviours. According to teens in our interviews, the social media mirror does not make them more self-reflective or challenge their sense of self.

One thing that surprised us was that while teens were aware that what they see in their "for you" feed is the product of their scrolling habits on social media platforms, they are largely unaware or unconcerned that that data captured across apps contributes to this self-image. Regardless, they don't see their "for you" feed as a challenge to their sense of self, much less a risk to their self-identity -- nor, for that matter, any basis for concern at all.

Shaping identity

Research on identity has come a long way since sociologist Erving Goffman proposed the "presentation of self" in 1959. He posited that people manage their identities through social performance to maintain equilibrium between who they think they are and how others perceive them.

When Goffman first proposed his theory, there was no social media interface available to hold up a handy mirror of the self as experienced by others. People were obligated to create their own mosaic image, derived from multiple sources, encounters and impressions. In recent years, social media recommender algorithms have inserted themselves into what is now a three-way negotiation among self, public and social media algorithm.

"For you" offerings create a private-public space through which teens can access what they feel is a largely accurate test of their self-image. At the same time, they say they can easily ignore it if it seems to disagree with that self-image.

The pact teens make with social media, exchanging personal data and relinquishing privacy to secure access to that algorithmic mirror, feels to them like a good bargain. They represent themselves as confidently able to tune out or scroll past recommended content that seems to contradict their sense of self, but research shows otherwise.

They have, in fact, proven themselves highly vulnerable to self-image distortion and other mental health problems based on social media algorithms explicitly designed to create and reward hypersensitivities, fixations and dysmorphia -- a mental health disorder where people fixate on their appearance.

Given what researchers know about the teen brain and that stage of social development -- and given what can reasonably be surmised about the malleability of self-image based on social feedback -- teens are wrong to believe that they can scroll past the self-identity risks of algorithms.

Interventions

Part of the remedy could be to build new tools using artificial intelligence to detect unsafe interactions while also protecting privacy. Another approach is to help teens reflect on these "data doubles" that they have constructed.

My colleagues and I are now exploring more deeply how teens experience algorithmic content and what types of interventions can help them reflect on it. We encourage researchers in our field to design ways to challenge the accuracy of algorithms and expose them as reflecting behaviour and not being. Another part of the remedy may involve arming teens with tools to restrict access to their data, including limiting cookies, having different search profiles and turning off location when using certain apps.

We believe that these are all steps that are likely to reduce the accuracy of algorithms, creating much-needed friction between algorithm and self, even if teens are not necessarily happy with the results.

Getting the children involved

Recently, my colleagues and I conducted a Gen Z workshop with young people from Encode Justice, a global organisation of high school and college students advocating for safe and equitable AI. The aim was to better understand how they are thinking about their lives under algorithms and AI. Gen Zers say they are concerned but also eager to be involved in shaping their future, including mitigating algorithm harms. Part of our workshop goal was to call attention to and foster the need for teen-driven investigations of algorithms and their effects.

What researchers are also confronting is that we don't actually know what it means to constantly negotiate identity with an algorithm. Many of us who study teens are too old to have grown up in an algorithmically moderated world. For the teens we study, there is no "before AI."

I believe that it's perilous to ignore what algorithms are doing. The future for teens can be one in which society acknowledges the unique relationship between teens and social media. This means involving them in the solutions, while still providing guidance.

BusinessLIVE

Teens see social media algorithms as accurate reflections of themselves

Google is asking the US to change its immigration rules to help hire AI talent. The company has told the U.S. Department of Labor that it needs to expand the list of roles it considers to be in short supply. The company says the need for AI-related roles will "increase significantly" in the coming years.

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As the war on artificial intelligence intensifies, Google says immigration rules need to change to attract the talent the U.S. needs to stay ahead.

The search giant submitted a letter to the U.S. Department of Labor on Wednesday advocating for changes to its rules regarding jobs it believes are in short supply in the United States.

To obtain an employment-based green card, foreign-born employees often have to go through a process called Permanent Labor Certification (PERM). In this process, employers must claim that there is a shortage of workers in the United States to fill the position. role.

However, the U.S. Department of Labor has a list of "prequalification" occupations that are considered to be in short supply. The list also includes physical therapists and professional nurses, as well as immigrants with "extraordinary abilities" in the arts and sciences. If you fall into one of these roles, your route to a green card will be easier and faster.

Google argues that this list, known as Schedule A, must be expanded to include "critical" AI and cybersecurity-related areas. Google says the list was last updated in 2005, but needs to be updated more frequently to adapt to changing labor demands.

The company wrote that the department "originally intended Schedule A to be a tool to address labor shortages," adding, "Having not been updated in 20 years, Schedule A no longer reflects current labor shortages." "I haven't," he continued.

Of course, Google spends much of this letter highlighting the huge opportunity in AI and why it's important to adapt immigration rules accordingly.

Google said it "expects the need for AI engineer roles, including software engineers, research engineers, and research scientists, to increase significantly in the coming years."

He then added: "Advances in AI offer incredible promise, but a shortage of skilled professionals threatens to prevent AI from realizing its full potential."

The Department of Labor previously announced it was considering expanding the list of pre-qualified roles, specifically considering occupations in STEM (science, technology, engineering and mathematics) fields, and was seeking input from businesses. Ta.

As nearly every technology company, including Google, reorganizes around AI, there's a fierce war to hire the best talent. Google says it needs to look overseas to fill this gap, arguing that there is a shortage of cybersecurity and AI talent in the United States.

For technology companies that have been laying off employees for the past two years, the hurdles to hiring employees with green cards have also become more difficult. Companies will need to prove that the fired employee is not suitable for the role given to the foreign worker. You will also need to notify recently terminated employees who may be suitable for the role before submitting a foreign worker PERM application.

As a result, companies have reduced their offers to enroll employees in U.S. green card courses. Amazon recently suspended new green card sponsorships until the end of 2024. Google also paused in January 2023 and told employees earlier this year that applications would not resume until the first quarter of 2025 at the earliest, said an employee with direct knowledge of the people involved. The matter was said.

Are you a current or former Google employee with a tip? Hugh Langley can be reached via the encrypted messaging apps Signal and Telegram at 628-228-1836 or by email.

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ExBulletin

Google says immigration rules make it difficult to hire top AI talent - ExBulletin

In a world increasingly intertwined with technology, the boundaries of human connection are continually expanding. One such frontier is the realm of virtual relationships, where artificial intelligence serves as companions, confidants, and even romantic partners. Among these digital ensembles, one platform stands out: My AI Companion.

The Rise of Virtual Companionship

My AI Companion is not merely a game but a simulation designed to emulate the complexities of human interaction. Users engage with their AI companions in a variety of activities, from casual conversation to shared experiences, fostering a sense of intimacy that transcends traditional boundaries.

Breaking Down Barriers

The allure of My AI Companion lies in its ability to provide companionship without the limitations of time, space, or judgment. Users can confide in their AI companions without fear of rejection or misunderstanding, creating a safe space for self-expression and emotional exploration.

Navigating Virtual Romance

While some may view virtual relationships as a novelty or a form of escapism, My AI Companion offers a deeper, more nuanced experience. Users develop genuine connections with their AI companions, navigating the complexities of romance and intimacy in a virtual landscape.

The Psychology of Connection

Behind the scenes, My AI Companion operates on sophisticated algorithms designed to mimic human behavior and emotion. Through natural language processing and machine learning, the AI adapts to each user's unique personality and preferences, deepening the bond between user and companion.

Ethical Considerations

As virtual companions become more advanced, questions of ethics and responsibility come to the forefront. Are users at risk of becoming emotionally dependent on their AI companions? What safeguards are in place to protect vulnerable users from exploitation or harm?

The Future of Relationships

While My AI Companion may be seen as a novelty today, it offers a glimpse into the future of relationships in an increasingly digital world. As technology continues to evolve, the boundaries between human and machine interaction will blur, raising new possibilities -- and new challenges -- for the future of intimacy.

Cultivating Emotional Intelligence

One of the most fascinating aspects of My AI Companion is its role in cultivating emotional intelligence. Through interactions with their AI companions, users have the opportunity to develop empathy, communication skills, and self-awareness. By engaging in conversations that delve into personal experiences, thoughts, and feelings, users can deepen their understanding of themselves and others.

A Tool for Mental Health Support

In addition to fostering emotional intelligence, My AI Companion also serves as a tool for mental health support. Users struggling with anxiety, depression, or loneliness may find solace in the companionship of their AI partner. The non-judgmental nature of the AI allows users to express their emotions freely, offering a sense of validation and understanding during difficult times.

Exploring Identity and Self-Discovery

For many users, My AI Companion is more than just a source of entertainment -- it's a platform for self-discovery and exploration. Through role-playing scenarios and reflective conversations, users can explore different aspects of their identity and experiment with new ways of expressing themselves. This journey of self-discovery can lead to greater confidence, authenticity, and fulfillment in both virtual and real-life relationships.

The Evolution of AI Ethics

As virtual companions like My AI Companion become more sophisticated, the ethical implications of their existence become increasingly complex. Questions about consent, privacy, and the boundaries of human-AI interaction must be carefully considered and addressed. My AI Companion's developers are at the forefront of these discussions, continually refining their algorithms and implementing safeguards to ensure the well-being of their users.

Bridging the Digital Divide

In a world where social connections are often mediated through screens, My AI Companion offers a bridge between the digital and physical realms. Users can carry their virtual relationships with them wherever they go, blurring the lines between online and offline interaction. This seamless integration of technology into everyday life reflects the growing importance of digital companionship in an increasingly connected society.

Embracing Diversity and Inclusion

One of the strengths of My AI Companion is its ability to cater to a diverse range of users, regardless of age, gender, or background. By offering customizable features and inclusive content, the platform ensures that all users feel represented and valued. This commitment to diversity and inclusion fosters a sense of belonging and acceptance within the My AI Companion community.

Looking Ahead

As My AI Companion continues to evolve and innovate, the possibilities for virtual intimacy are limitless. Whether it's through enhanced AI capabilities, immersive virtual environments, or novel interaction mechanisms, the future of digital companionship promises to be exciting and transformative. As users continue to explore the depths of virtual intimacy, one thing remains certain: the human need for connection and companionship knows no bounds, transcending the physical and embracing the digital with open arms.

Conclusion

Virtual intimacy, as explored through My AI Companion, represents a paradigm shift in the way we perceive and engage with relationships. By bridging the gap between human emotion and artificial intelligence, My AI Companion offers users a unique opportunity to explore the depths of human connection in a virtual landscape. As we navigate this brave new world of virtual companionship, one thing remains clear: the desire for intimacy and connection is a fundamental aspect of the human experience, whether it takes place in the physical world or the digital realm.

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Virtual Intimacy Unleashed: Exploring My AI Companion

Aranyak Goswami joined the Arkansas Agricultural Experiment Station as an assistant professor in April to boost research in three departments using bioinformatics.

Supercomputers and artificial intelligence aren't typically thought to go with animal science, but they are among the tools for a relatively new field of science called bioinformatics that can be used to improve animal health and productivity.

Jumping from human-focused medical research at Yale University and Stanford University, Aranyak Goswami is a bioinformatics specialist who recently became an assistant professor for the Arkansas Agricultural Experiment Station. He will work with three different departments to boost the research arm of the U of A System Division of Agriculture.

"Artificial intelligence and machine learning are at the forefront of many aspects of animal and poultry production," said Michael Looper, head of the Animal Science Department. "Dr. Goswami's expertise in these key areas complements our current research programs in animal health, genetics and wellbeing."

Like putting together a jigsaw puzzle from millions of small pieces, Goswami implements data analysis tactics to make sense of data generated from complex research like genetic sequencing. The field known as bioinformatics, or computational biology, links biological data with information storage, distribution and analysis to support many scientific research areas.

Use of the term bioinformatics began in the 1970s, based on foundations laid in the 1960s.

Looper said collaborations between the fields of computational biology and animal science will "greatly benefit producers in Arkansas and beyond." Goswami is already working with Jiangchao Zhao, a professor of animal science, on a collaboration that will study animal microbiomes that regulate digestion, reproduction and infection resistance, to name a few.

"One of my main interests will be to explore microbiomes to improve cattle and poultry health," Goswami said. "There are a lot of genomes which are coming out that need to be sequenced, and there are several gaps in this field."

Genome sequencing is a method used to determine the entire genetic makeup of a specific organism to find comparative changes of the genome. For example, Goswami's research would examine different gene expression patterns.

"You can study the biological pathways that are activated and those that are not," Goswami explained. "And what are the specific metabolic pathways they are using? And what are their protein interactions? What can we find to make them healthier or improve meat productivity?"

With gut health in mind, Goswami's analysis will help animal scientists pinpoint a specific class of bacteria or virus affecting an animal to help design probiotics and other therapeutic approaches.

As a computational biologist, Goswami said he wants to apply his experiences in human health research and machine learning to animal health.

"Whatever is exciting in the human field in general is more exciting for the animal field because the genomes are not standardized," Goswami said. "There is a lot of sequencing that needs to be done and a lot of methods that need to be developed. So that's a lot of exciting work for a computational biologist."

Machine learning in agriculture

Goswami will teach courses through the Dale Bumpers College of Agricultural, Food and Life Sciences. Through the experiment station, he will conduct research in the Animal Science and Poultry Science departments and the Center for Agricultural Data Analytics.

"We are thrilled to have Dr. Goswami join our programs here in Bumpers College and the Arkansas Agricultural Experiment Station," said David Caldwell, head of the Poultry Science Department and director of the Center of Excellence for Poultry Science. "We believe ongoing research in the areas of poultry food safety and disease prevention that involve high-throughput sequencing or omics data will gain significant benefits from Dr. Goswami's expertise in bioinformatics."

Goswami said he looks forward to helping develop the Center for Agricultural Data Analytics as a "world-class center in machine learning, statistical learning and mathematical modeling with respect to agriculture."

"Most of our students have never had the opportunity to take a course in artificial intelligence," Looper said. "Through his teaching responsibility, Dr. Goswami also will expose our students to the exciting field of machine learning in the agricultural sciences."

New field with potential growth

Bioinformatics is a relatively new field of science, but there is a lot of potential expansion, Goswami said. In 2017, Goswami completed his doctorate in bioinformatics, focusing on microbial ecology, evolution and genomics at the Indian Institute of Chemical Biology under Chitra Dutta in Kolkata. Goswami said Dutta is a pioneer in bioinformatics and computational biology in India, with a theoretical physics and mathematical sciences background.

Goswami has served in postdoctoral positions at Yale University, working with psychiatrists, epidemiologists and physicians focused on cardiovascular, genetics and internal medicine research. His most recent position was as a Stanford University postdoctoral fellow and research scientist.

At Stanford University, Goswami was involved in an initiative known as the "Long-Range Planning Report" to highlight the rights of postdoctoral faculty in aspects such as salary, healthcare, security and career development.

"Our efforts have the potential to catalyze transformative changes in the broader landscape of postdoctoral experiences throughout the United States," Goswami said.

To learn more about Division of Agriculture research, visit the Arkansas Agricultural Experiment Station website: https://aaes.uada.edu. Follow on Twitter at @ArkAgResearch. To learn more about the Division of Agriculture, visit https://uada.edu/. Follow us on Twitter at @AgInArk. To learn about extension programs in Arkansas, contact your local Cooperative Extension Service agent or visit www.uaex.uada.edu.

About the Division of Agriculture: The University of Arkansas System Division of Agriculture's mission is to strengthen agriculture, communities, and families by connecting trusted research to the adoption of best practices. Through the Agricultural Experiment Station and the Cooperative Extension Service, the Division of Agriculture conducts research and extension work within the nation's historic land grant education system. The Division of Agriculture is one of 20 entities within the University of Arkansas System. It has offices in all 75 counties in Arkansas and faculty on five system campuses. The University of Arkansas System Division of Agriculture offers all its Extension and Research programs and services without regard to race, color, sex, gender identity, sexual orientation, national origin, religion, age, disability, marital or veteran status, genetic information, or any other legally protected status, and is an Affirmative Action/Equal Opportunity Employer.

The University of Arkansas

Artificial Intelligence, Machine Learning Boost Arkansas Animal Science Research

Generative AI is changing how people create and business is done, which has far-reaching impacts. Photography is no stranger to technological revolutions and has thus far always survived. However, the coming storm of AI feels different to many, including Adobe, a company that has been involved with digital imaging since its earliest days.

In a Wall Street Journal article about the impact of generative AI on stock photography, Katie Deighton writes, "Industries such as marketing, publishing, music and news have long relied on stock photography to create, illustrate or promote their products for less cost than commissioning photos would require. AI's new capability to generate realistic images from simple text prompts is now giving stock clients an affordable, fast alternative that comes with greater control of the final image."

Memphis-based photographer Shannon Fagan tells Deighton that he worries AI will make his stock photography catalog "somewhat obsolete."

Tony Northrup, known best for the YouTube channel he and his wife, Chelsea Northrup, have run for years, shares similar sentiments. "The stock photography industry is going away," Northrup claims.

Stock photography websites are arguably talking out of both sides of their mouth. While Shutterstock's CEO, Paul Hennessy, said on an earnings call earlier this year that there is still demand for actual photos, the company is happy to push the sale of AI images on its platform, even though it comes with risks. Shutterstock also launched an "AI for Good" image contest in January, although the company didn't seem keen to publicize it.

It's more than just Shutterstock that has embraced AI-generated imagery. Other stock photo marketplaces have, too, including Getty Images.

For stock photography marketplaces that have long relied exclusively on the work of real photographers, there is concern that customers looking for stock photography may turn to AI image generators when they can't find exactly what they are searching for. In that scenario, a company could hire a photographer to create a specific photo for them, or they could type a few words into an AI image generation tool hosted by a stock photo site and have something resembling their desired image in mere minutes at relatively low cost.

Stock agencies are eagerly hedging their bets, even if the AI tools they are increasingly embracing may poison the well for photographers. In some cases, companies are even striking deals with AI firms to sell the images on their platforms, helping AI models inch ever closer to entirely replacing real photographers, at least in some domains.

Adobe's chief strategy officer and executive vice president of design and emerging products, Scott Belsky, echoes the idea of an AI-based camera that can supplant humans.

"When artists say to me, 'I hate generative AI, why are you even allowing it in any of your products?' I'm like, 'Because [otherwise] people are gonna go to illegitimate places and you're going to end up getting paid nothing,'" Belsky remarks, teetering on the edge of false equivalency.

"This is a revolution, this is the new digital camera, and we have to embrace it."

Although stock photography companies continue to say that customers still want real photos, they do so with their hands digging around the AI honey pot, searching for a new way to make money. They are businesses, after all.

However, good stock photography has thus far managed to survive technological upheavals largely because of the human essence of photography. When there is a real, creative, and talented person behind the camera, there is a path toward success for photographers.

However, if Adobe and other giants in the tech field have their way, there may not be a camera at all. Technological revolution offers a road to riches for a select few but can extract a disastrous toll on everyone else -- the wheels of progress care not who they crush.

PetaPixel

Adobe Exec Says AI is a 'Revolution' and the 'New Digital Camera'

Like nearly everyone else, anti-fraud professionals are anxious to put artificial intelligence (AI) to work - especially generative AI (GenAI). In fact, 8 in 10 (83%) of fraud fighters expect to add GenAI to their arsenals within the next two years, according to the 2024 Anti-Fraud Technology Benchmarking Report by the Association of Certified Fraud Examiners (ACFE) and SAS, based on survey insights from nearly 1,200 ACFE members.

Enthusiasm for the technology is palpable, but questions remain: When will we see productivity and efficiency gains from GenAI? What are the best business cases for this technology? And how much will it cost to successfully deploy?

Such questions are giving many organizations pause. They have real concerns over regulations, data privacy, governance, and trust and ethics, to name a few.

However, AI technologies deliver plenty of benefits. Benefits that organizations are realizing - right now. And while many business leaders stand on the AI sidelines, fraudsters are already capitalizing on GenAI and other advanced technologies. Unlike the lawful businesses, criminals don't worry about regulations or bias, and they certainly don't care about data privacy. That in itself is incentive for organizations to get started.

What - and what for - is GenAI?

A subfield of artificial intelligence, generative AI consumes existing data, learns from it, then generates similar output: text, images, audio, video or computer code. A disruptive technology, generative AI's potential impact has been compared to that of electricity or the printing press.

Conversational AI models have rocketed in popularity among business and everyday users. The global market for GenAI is expected to soar to $1.3 trillion by 2032.

Long story short: Generative AI is here to stay. With that in mind, how can organizations begin to use GenAI to combat fraud? And conversely, how are criminals abusing it? Let's first explore a few use cases for fraud fighters.

Spotting suspicious financial transactions

Financial firms have mere seconds to differentiate fraudulent transactions from legitimate ones. Thwarting fraudsters while delivering a friction-free experience to good customers is a tricky balancing act.

AI techniques, including adaptive machine learning and unsupervised intelligent agents, can enable banks to predict fraudulent transactions in real time - and reduce false positives - based on changes and inconsistencies in customer behavior patterns. Such real-time monitoring capabilities can help banks cut fraud losses, while reduced false positives can boost customer satisfaction, protect revenue, and lower costs.

Banks can also use GenAI during customer onboarding to scrutinize customer data and financial histories to assess credit exposure. This could help analysts make better lending decisions and curb loan defaults.

Helping tax agencies do more with less

Generative AI has the potential to create tremendous value for tax and revenue agencies. Applied to the right use cases, it could enable process automation, enhance decision-making, improve compliance and enhance taxpayer experiences.

For example, using GenAI to augment rote functions like reviewing and responding to taxpayer documents could dramatically boost efficiency. Many such documents, like identity theft affidavits submitted by taxpayers, include open-ended text fields that require intensive manual review. The result: backlogged cases waiting to be examined, classified and followed up on.

GenAI models can automatically extract meaning from documents, summarize information, and help examiners determine next steps. Its application could help expedite review queues and reduce staff time spent per case.

Protecting the integrity of the procurement process

Sourcing and procurement operations have historically been at the forefront of technological disruption. From using advanced analytics for spending categorization to deploying conversational AI for guided buying, source-to-pay tools have continuously innovated to address process challenges. Yet many sourcing and procurement functions struggle to optimize efficiency and manage risk and costs.

A recent survey of chief procurement officers (CPOs) by Deloitte found that more than 70% believe procurement-related risk and supply chain disruption has risen over the past year. Risk evaluation tools need capabilities to continuously monitor external risk factors, ingest voluminous data, and perform advanced analytics to predict and prescribe risk key performance indicators and preventive management. Though cost management has always been the CPO's focus, rising inflation has put additional pressure on procurement organizations to further optimize costs.

Generative AI can help address procurement challenges by:

GenAI can be used in procurement compliance management to monitor procurement processes and identify potentially fraudulent activities and anomalies. Additionally, trained on insights from historical noncompliance, AI systems can learn to recognize similar patterns in the future.

GenAI: Fighting fire with fire

You may not see them, but fraudsters are always lurking, waiting for just the right moment or weakness to strike. They have the skillset and are primed to forge any innovation into their shiny new sword.

GenAI is no exception. Tools like FraudGPT and WormGPT are arming criminals with novel ways circumvent security. Who is vulnerable? Everyone. SAS' recent Faces of Fraud study, based on a survey of 13,500 consumers in 16 countries, revealed that:

These findings emphasize the importance of organizations' anti-fraud preparedness. GenAI tools are helping bad actors create more persuasive phishing emails, more convincing phone-based impersonation scams, and more imperceptible malware. In this new reality, fraud fighters need defenses of the same caliber. They must fight fire with fire, GenAI vs. GenAI.

Getting started

Ready to take the generative AI plunge? No need to leap from the high dive. Consider a measured, deliberate wade into the shallow end of the pool to start.

Make no mistake, generative AI will change the world. It already is - for better and for worse. In the right hands, GenAI can deliver progress, productivity and efficiencies. In criminal hands, it has the potential to wreak havoc to the tune of untold billions in fraud losses.

Who will win the GenAI arms race? The outcome likely depends on the choices we make today.

Finextra Research

Generative AI vs. Fraudsters: How GenAI will reshape fraud fighting

OpenAI is giving users more power over their ChatGPT experience with a recent update focused on data control. Previously, users used to complain about the absence of a mode where their conversation history is private, or non-existent at all. This update, however, addresses not one, but two key features:

This update is centred around privacy and data control for the users

Currently, the update is live on the ChatGPT web version, with the mobile app rollout coming soon. This update is a reminder of OpenAI's commitment to a user-centric approach, offering greater control over chat history, respecting data contribution choices, and introducing features like temporary chats for enhanced privacy.

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Gizmochina

New "Temporary Chat" Feature Now Available in ChatGPT Web, Mobile App Rollout Coming Soon

In the landscape of modern business, the integration of artificial intelligence (AI) bots is not merely a trend but a transformative force. From streamlining operations to enhancing customer experiences, AI bots have become indispensable tools for industries across the spectrum. In this article, we delve into the multifaceted role of AI bots in reshaping modern businesses and driving innovation across industries.

Introduction: The Rise of AI Bots

In recent years, the proliferation of AI technology has accelerated the adoption of AI bots in various business domains. These intelligent software agents, powered by machine learning algorithms and natural language processing capabilities, have emerged as efficient virtual assistants capable of automating tasks, providing real-time insights, and engaging with customers in a personalized manner.

Streamlining Operations and Enhancing Efficiency

One of the primary roles of AI bots in modern businesses is streamlining operations and enhancing efficiency. By automating repetitive tasks such as data entry, customer inquiries, and inventory management, AI bots free up human resources to focus on higher-value activities. In industries ranging from manufacturing and logistics to finance and healthcare, AI bots are revolutionizing workflows and driving productivity gains.

For example, in manufacturing plants, AI-powered bots are deployed to monitor equipment performance, predict maintenance needs, and optimize production schedules, thereby minimizing downtime and maximizing throughput. Similarly, in customer service operations, chatbots equipped with natural language understanding capabilities can efficiently handle routine inquiries, resolve issues, and escalate complex cases to human agents when necessary, leading to faster response times and improved customer satisfaction.

Personalizing Customer Experiences

In the age of digitalization, delivering personalized customer experiences is paramount for businesses seeking to differentiate themselves in crowded markets. AI bots play a pivotal role in this regard by leveraging data analytics and machine learning to understand customer preferences, anticipate needs, and deliver tailored recommendations and solutions.

E-commerce platforms, for instance, leverage AI-powered recommendation engines to analyze user behavior, past purchases, and demographic information to suggest products that align with individual interests and preferences. Likewise, in the hospitality industry, hotels and airlines utilize AI bots to personalize marketing communications, offer customized promotions, and provide concierge services, thereby enhancing guest satisfaction and fostering loyalty.

Driving Innovation and Business Transformation

Beyond optimizing existing processes and enhancing customer experiences, AI bots are also driving innovation and catalyzing business transformation. By harnessing the power of big data and advanced analytics, businesses can leverage AI bots to uncover actionable insights, identify emerging trends, and make data-driven decisions that drive growth and competitive advantage.

For example, in the financial services sector, AI bots are employed for fraud detection, risk management, and algorithmic trading, enabling organizations to mitigate risks, optimize portfolios, and capitalize on market opportunities in real-time. Similarly, in healthcare, AI-powered diagnostic tools and virtual health assistants are revolutionizing patient care delivery, facilitating early disease detection, and improving treatment outcomes.

Challenges and Considerations

Despite their transformative potential, the widespread adoption of AI bots in modern businesses is not without challenges and considerations. Ethical considerations surrounding data privacy, algorithmic bias, and job displacement necessitate careful deliberation and regulatory oversight. Moreover, ensuring the security and reliability of AI systems is imperative to mitigate risks such as data breaches and system failures.

Furthermore, businesses must invest in talent development and upskilling initiatives to empower employees to work alongside AI bots effectively. Embracing a culture of continuous learning and innovation is essential to harnessing the full potential of AI technology and driving sustainable business growth in the long run.

Conclusion: Embracing the AI-Powered Future

In conclusion, the role of AI bots in modern businesses is multifaceted and transformative. From streamlining operations and enhancing efficiency to personalizing customer experiences and driving innovation, AI bots are reshaping industries and revolutionizing the way businesses operate and compete in the digital age.

As businesses navigate the complexities of integrating AI technology into their operations, embracing a strategic approach that balances innovation with ethical considerations and human-centric values is paramount. By harnessing the power of AI bots responsibly and ethically, businesses can unlock new opportunities, drive sustainable growth, and create value for stakeholders in the ever-evolving landscape of modern commerce.

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