#AI Development Company in US

[alignminds]

Hire AI Developer for your Businesss

[smtlabsio]

SMTLabs is your trusted Artificial Intelligence Development Company. We specialize in Custom AI Software Development and offer cutting-edge Artificial Intelligence Development Services. Our Custom AI Software Development solutions serve your specific requirements. Our professional team of AI developers empowers you to harness the true potential of AI technology.

[xeed-null]

If one was extremely determined and foolish, they could create an AI by hand. That is to say, one could map out all of a generative AI on paper because it is all math and numbers. It would probably take a lifetime to 'generate' just one thing; after all the mathematician in question would have to work through the tedium of the entire cross-referenced dataset.

This illuminates why generative AI uses so many resources, it's doing a human lifetime of work for just an incredibly small amount of stuff.

it's impressive that it can do so much so fast for so little output and so much waste.

[crazy-hazy-sims]

for the first time in my entire life someone actually respected my desires and how i choose to live ( and ofc its non other than my brother)

i got asked by my dad why dont i go to the gym so i can become slim fit and healthier? i get this alot it doesn't phase me so i said " i do not have time i work alot" he pressed on and said you do have time!!! your days off!!! and he proceeded to count all the days i stay at home (including the days i work from home and he wouldn't accept that work from home days are not vacation days) and i answered " 1- i AM busy and 2- i prefer to fill the few hours i have off with things i like like my hobbies and gaming and reading" my dad scoffed but my brother looked at me nodded and said "that's fair actually!" and then decided to engage me in a conversation about my hobbies (video games and art) which led to him suggesting that i take my time to develop my art so i eventually could work in the game industry with my portfolio.

that's it !! that's all i ever wanted from people all i ever asked to be respected this way... sigh

[shoechoe]

i find the common implication that Google of all companies wasn't already using up a ton of water and energy before its implementation of AI annoying but it is true that the AI results feature is bad and useless and therefore an extra waste of water and energy for no practical purpose. i would have no problem with posts that just say that instead of bizarrely wording it like "well google USED to be fine but NOW thanks to AI it uses a million billion tons of water"

[daemon-in-my-head]

Just saw the news regarding twitters updated terms of service and AI training

Glad that I'm solely using it for work 9/10 times anyway. The chances of me posting there were low, but consider them pretty much 0 atp lmfao

[lillyanne4writes]

In 2022, 2.2 billion people didn't have access to safely managed drinking water (source). You'd think doing something about that would be our priority, but no.

(Btw, here's the full article by The Standard shown in the screenshot. It's worth checking out for more details, including a few measures that tech companies could or are planning to take to tackle the problem.)

[alignminds]

Looking to hire an expert AI Developer in the US? Find skilled professionals proficient in machine learning, NLP, and deep learning to build intelligent solutions that drive innovation. Available for full-time, part-time, or project-based roles.

[jcmarchi]

Denas Grybauskas, Chief Governance and Strategy Officer at Oxylabs – Interview Series

New Post has been published on https://thedigitalinsider.com/denas-grybauskas-chief-governance-and-strategy-officer-at-oxylabs-interview-series/

Denas Grybauskas, Chief Governance and Strategy Officer at Oxylabs – Interview Series

Denas Grybauskas is the Chief Governance and Strategy Officer at Oxylabs, a global leader in web intelligence collection and premium proxy solutions.

Founded in 2015, Oxylabs provides one of the largest ethically sourced proxy networks in the world—spanning over 177 million IPs across 195 countries—along with advanced tools like Web Unblocker, Web Scraper API, and OxyCopilot, an AI-powered scraping assistant that converts natural language into structured data queries.

You’ve had an impressive legal and governance journey across Lithuania’s legal tech space. What personally motivated you to tackle one of AI’s most polarising challenges—ethics and copyright—in your role at Oxylabs?

Oxylabs have always been the flagbearer for responsible innovation in the industry. We were the first to advocate for ethical proxy sourcing and web scraping industry standards. Now, with AI moving so fast, we must make sure that innovation is balanced with responsibility.

We saw this as a huge problem facing the AI industry, and we could also see the solution. By providing these datasets, we’re enabling AI companies and creators to be on the same page regarding fair AI development, which is beneficial for everyone involved. We knew how important it was to keep creators’ rights at the forefront but also provide content for the development of future AI systems, so we created these datasets as something that can meet the demands of today’s market.

The UK is in the midst of a heated copyright battle, with strong voices on both sides. How do you interpret the current state of the debate between AI innovation and creator rights?

While it’s important that the UK government favours productive technological innovation as a priority, it’s vital that creators should feel enhanced and protected by AI, not stolen from. The legal framework currently under debate must find a sweet spot between fostering innovation and, at the same time, protecting the creators, and I hope in the coming weeks we see them find a way to strike a balance.

Oxylabs has just launched the world’s first ethical YouTube datasets, which requires creator consent for AI training. How exactly does this consent process work—and how scalable is it for other industries like music or publishing?

All of the millions of original videos in the datasets have the explicit consent of the creators to be used for AI training, connecting creators and innovators ethically. All datasets offered by Oxylabs include videos, transcripts, and rich metadata. While such data has many potential use cases, Oxylabs refined and prepared it specifically for AI training, which is the use that the content creators have knowingly agreed to.

Many tech leaders argue that requiring explicit opt-in from all creators could “kill” the AI industry. What’s your response to that claim, and how does Oxylabs’ approach prove otherwise?

Requiring that, for every usage of material for AI training, there be a previous explicit opt-in presents significant operational challenges and would come at a significant cost to AI innovation. Instead of protecting creators’ rights, it could unintentionally incentivize companies to shift development activities to jurisdictions with less rigorous enforcement or differing copyright regimes. However, this does not mean that there can be no middle ground where AI development is encouraged while copyright is respected. On the contrary, what we need are workable mechanisms that simplify the relationship between AI companies and creators.

These datasets offer one approach to moving forward. The opt-out model, according to which content can be used unless the copyright owner explicitly opts out, is another. The third way would be facilitating deal-making between publishers, creators, and AI companies through technological solutions, such as online platforms.

Ultimately, any solution must operate within the bounds of applicable copyright and data protection laws. At Oxylabs, we believe AI innovation must be pursued responsibly, and our goal is to contribute to lawful, practical frameworks that respect creators while enabling progress.

What were the biggest hurdles your team had to overcome to make consent-based datasets viable?

The path for us was opened by YouTube, enabling content creators to easily and conveniently license their work for AI training. After that, our work was mostly technical, involving gathering data, cleaning and structuring it to prepare the datasets, and building the entire technical setup for companies to access the data they needed. But this is something that we’ve been doing for years, in one way or another. Of course, each case presents its own set of challenges, especially when you’re dealing with something as huge and complex as multimodal data. But we had both the knowledge and the technical capacity to do this. Given this, once YouTube authors got the chance to give consent, the rest was only a matter of putting our time and resources into it.

Beyond YouTube content, do you envision a future where other major content types—such as music, writing, or digital art—can also be systematically licensed for use as training data?

For a while now, we have been pointing out the need for a systematic approach to consent-giving and content-licensing in order to enable AI innovation while balancing it with creator rights. Only when there is a convenient and cooperative way for both sides to achieve their goals will there be mutual benefit.

This is just the beginning. We believe that providing datasets like ours across a range of industries can provide a solution that finally brings the copyright debate to an amicable close.

Does the importance of offerings like Oxylabs’ ethical datasets vary depending on different AI governance approaches in the EU, the UK, and other jurisdictions?

On the one hand, the availability of explicit-consent-based datasets levels the field for AI companies based in jurisdictions where governments lean toward stricter regulation. The primary concern of these companies is that, rather than supporting creators, strict rules for obtaining consent will only give an unfair advantage to AI developers in other jurisdictions. The problem is not that these companies don’t care about consent but rather that without a convenient way to obtain it, they are doomed to lag behind.

On the other hand, we believe that if granting consent and accessing data licensed for AI training is simplified, there is no reason why this approach should not become the preferred way globally. Our datasets built on licensed YouTube content are a step toward this simplification.

With growing public distrust toward how AI is trained, how do you think transparency and consent can become competitive advantages for tech companies?

Although transparency is often seen as a hindrance to competitive edge, it’s also our greatest weapon to fight mistrust. The more transparency AI companies can provide, the more evidence there is for ethical and beneficial AI training, thereby rebuilding trust in the AI industry. And in turn, creators seeing that they and the society can get value from AI innovation will have more reason to give consent in the future.

Oxylabs is often associated with data scraping and web intelligence. How does this new ethical initiative fit into the broader vision of the company?

The release of ethically sourced YouTube datasets continues our mission at Oxylabs to establish and promote ethical industry practices. As part of this, we co-founded the Ethical Web Data Collection Initiative (EWDCI) and introduced an industry-first transparent tier framework for proxy sourcing. We also launched Project 4β as part of our mission to enable researchers and academics to maximise their research impact and enhance the understanding of critical public web data.

Looking ahead, do you think governments should mandate consent-by-default for training data, or should it remain a voluntary industry-led initiative?

In a free market economy, it is generally best to let the market correct itself. By allowing innovation to develop in response to market needs, we continually reinvent and renew our prosperity. Heavy-handed legislation is never a good first choice and should only be resorted to when all other avenues to ensure justice while allowing innovation have been exhausted.

It doesn’t look like we have already reached that point in AI training. YouTube’s licensing options for creators and our datasets demonstrate that this ecosystem is actively seeking ways to adapt to new realities. Thus, while clear regulation is, of course, needed to ensure that everyone acts within their rights, governments might want to tread lightly. Rather than requiring expressed consent in every case, they might want to examine the ways industries can develop mechanisms for resolving the current tensions and take their cues from that when legislating to encourage innovation rather than hinder it.

What advice would you offer to startups and AI developers who want to prioritise ethical data use without stalling innovation?

One way startups can help facilitate ethical data use is by developing technological solutions that simplify the process of obtaining consent and deriving value for creators. As options to acquire transparently sourced data emerge, AI companies need not compromise on speed; therefore, I advise them to keep their eyes open for such offerings.

 Thank you for the great interview, readers who wish to learn more should visit Oxylabs.

[vague-humanoid]

At the California Institute of the Arts, it all started with a videoconference between the registrar’s office and a nonprofit.

One of the nonprofit’s representatives had enabled an AI note-taking tool from Read AI. At the end of the meeting, it emailed a summary to all attendees, said Allan Chen, the institute’s chief technology officer. They could have a copy of the notes, if they wanted — they just needed to create their own account.

Next thing Chen knew, Read AI’s bot had popped up inabout a dozen of his meetings over a one-week span. It was in one-on-one check-ins. Project meetings. “Everything.”

The spread “was very aggressive,” recalled Chen, who also serves as vice president for institute technology. And it “took us by surprise.”

The scenariounderscores a growing challenge for colleges: Tech adoption and experimentation among students, faculty, and staff — especially as it pertains to AI — are outpacing institutions’ governance of these technologies and may even violate their data-privacy and security policies.

That has been the case with note-taking tools from companies including Read AI, Otter.ai, and Fireflies.ai.They can integrate with platforms like Zoom, Google Meet, and Microsoft Teamsto provide live transcriptions, meeting summaries, audio and video recordings, and other services.

Higher-ed interest in these products isn’t surprising.For those bogged down with virtual rendezvouses, a tool that can ingest long, winding conversations and spit outkey takeaways and action items is alluring. These services can also aid people with disabilities, including those who are deaf.

But the tools can quickly propagate unchecked across a university. They can auto-join any virtual meetings on a user’s calendar — even if that person is not in attendance. And that’s a concern, administrators say, if it means third-party productsthat an institution hasn’t reviewedmay be capturing and analyzing personal information, proprietary material, or confidential communications.

“What keeps me up at night is the ability for individual users to do things that are very powerful, but they don’t realize what they’re doing,” Chen said. “You may not realize you’re opening a can of worms.“

The Chronicle documented both individual and universitywide instances of this trend. At Tidewater Community College, in Virginia, Heather Brown, an instructional designer, unwittingly gave Otter.ai’s tool access to her calendar, and it joined a Faculty Senate meeting she didn’t end up attending. “One of our [associate vice presidents] reached out to inform me,” she wrote in a message. “I was mortified!”

[espirittech]

Why Python AI and Machine Learning Services Are Essential for Industries

In today’s rapidly evolving technological landscape, the importance of Python AI and machine learning services in the USA cannot be overstated. As businesses across various sectors seek to harness the power of artificial intelligence (AI) and machine learning (ML) to gain a competitive edge, the demand for proficient machine learning with Python services has surged. This article explores the significance of these services, highlights the best Python AI machine learning services in the USA, and examines the top AI and ML companies providing these services. The purpose of this article is to provide insights into how organizations can leverage Python-based AI and machine learning solutions to enhance their operational efficiency and drive growth.our website: www.espirittech.com

[zynetoglobaltechnologies]

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[hotelasian]

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[siteitnow]

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[gurutechnolabs]

What are Some Successful Use Cases of Generative AI?

Generative AI is reshaping various sectors by empowering machines to produce original content, tackle challenges, and enhance workflows. Its innovative applications span from content generation to the healthcare industry. In this article, we will delve into some of the most impactful examples of generative AI and its transformative effects across different domains. If you're looking to harness these technologies, generative AI development services can provide customized solutions for your organization.

Content Creation

Generative AI has become a go-to solution for producing top-notch content quickly. Applications such as ChatGPT and Jasper AI can generate articles, product descriptions, and marketing materials in just a matter of minutes. Beyond text, generative AI excels in crafting eye-catching visuals as well. For instance, DALL·E can create unique images, while video editing tools like Runway streamline the process of video creation and editing. These features are essential for marketers, content creators, and designers who aim to boost their productivity and unleash their creative potential.

Personalized Recommendations

Generative AI has become a key player in enhancing user experiences on platforms such as Netflix, Spotify, and YouTube. By examining user preferences and behaviors, AI crafts personalized recommendations that resonate with each individual. This tailored approach not only increases user engagement but also heightens satisfaction, encouraging users to come back for more. In the realm of e-commerce, AI-powered product suggestions are driving sales by presenting customers with precisely what they desire.

Product Design and Prototyping

In fields such as fashion, architecture, and engineering, generative AI plays a crucial role in crafting cutting-edge products and prototypes. A prime example is Autodesk Generative Design, which leverages AI to develop the most efficient designs for production. This innovative approach not only saves time and cuts costs but also empowers companies to explore design possibilities that would have been unfeasible using conventional techniques.

Healthcare Innovations

Generative AI is revolutionizing the healthcare sector. In the realm of drug discovery, AI systems developed by companies such as OpenAI and DeepMind are sifting through data to uncover new chemical compounds at a pace that outstrips conventional techniques. This rapid analysis significantly speeds up the creation of life-saving drugs. Additionally, in medical imaging, AI is producing precise models that assist physicians in detecting abnormalities with greater accuracy, ultimately enhancing diagnoses and improving patient care.

Gaming and Virtual Reality

In the realm of gaming, generative AI plays a crucial role in crafting lifelike characters, vibrant environments, and compelling narratives. This technology enables developers to build dynamic game worlds that resonate with players, creating an immersive experience. In the contexts of virtual reality (VR) and augmented reality (AR), generative AI fuels simulations that elevate user interactions, making them more captivating and engaging.

Fraud Detection and Cybersecurity

Generative AI is helping businesses improve fraud detection and cybersecurity. It can simulate potential cyberattacks, allowing companies to build stronger defenses. Additionally, AI can create realistic phishing attempts to train systems in identifying and blocking threats. These proactive measures are crucial in safeguarding sensitive data and maintaining trust with customers.

Education and Learning

Generative AI is revolutionizing the educational landscape by offering customized learning experiences. AI-based platforms produce individualized study resources, quizzes, and explanations that cater to the unique requirements of each student. Additionally, AI-powered virtual tutors deliver immediate support, enhancing both accessibility and effectiveness in education. Educators and institutions gain from automated solutions that simplify curriculum design and evaluation processes.

Creative Arts

In the realm of creativity, generative AI is unlocking a world of new opportunities. Platforms such as Amper Music and AIVA enable musicians to create unique compositions, while AI-driven models assist writers in brainstorming scripts and story concepts. Visual artists can leverage generative AI to discover innovative styles or refine their current projects. These advancements inspire creatives to experiment boldly and expand the limits of their artistic expression.

Business Process Optimization

Companies are leveraging generative AI to enhance various processes, including data analysis, report creation, and financial predictions. For example, AI can sift through extensive datasets to produce valuable insights, allowing businesses to make swift, informed decisions. Additionally, marketing tactics, customer service operations, and supply chain management are becoming increasingly streamlined with the help of AI-driven solutions.

Emerging Use Cases

Generative AI is rapidly advancing, unveiling fresh applications in fields such as the creation of synthetic datasets for training AI models and the development of digital humans for virtual settings. These innovative technologies hold significant promise for transforming various industries, and companies can tap into these opportunities by utilizing generative AI development services customized to their specific requirements.

Challenges and Ethical Considerations

Generative AI presents remarkable advantages, but it also brings certain challenges. The potential for AI to be misused in creating deepfakes or disseminating false information raises significant ethical issues. It is crucial for businesses to develop AI solutions that emphasize fairness, transparency, and adherence to regulations. By collaborating with reputable generative AI development services, companies can adopt these technologies in a responsible and effective manner.

Conclusion

Generative AI is revolutionizing various sectors, including content creation, healthcare, gaming, and business efficiency. By examining these successful applications, companies can gain insights into how generative AI can address challenges and create fresh opportunities. With the support of expert generative AI development services, organizations can leverage AI's capabilities to enhance their operations and maintain a competitive edge in today's fast-paced environment.