Developed by researchers at MIT, Tsinghua University, and Canadian startup MyShell, OpenVoice uses just seconds of audio to clone a voice and allows granular control over tone, emotion, accent, rhythm, and more.  

MyShell unveiled OpenVoice in a post this week, linking to a pre-reviewed research paper explaining the technology as well as demo sites on MyShell and HuggingFace where users can try it.

Dual AI models enable instant voice cloning  

OpenVoice comprises two AI models working together for text-to-speech conversion and voice tone cloning.

The first model handles language style, accents, emotion, and other speech patterns. It was trained on 30,000 audio samples with varying emotions from English, Chinese, and Japanese speakers. The second “tone converter” model learned from over 300,000 samples encompassing 20,000 voices.

By combining the universal speech model with a user-provided voice sample, OpenVoice can clone voices with very little data. This helps it generate cloned speech significantly faster than alternatives like Meta’s Voicebox.

Canadian startup 

OpenVoice comes from Calgary-based startup MyShell, founded in 2023. With $5.6 million in early funding and over 400,000 users already, MyShell bills itself as a decentralised platform for creating and discovering AI apps.  

In addition to pioneering instant voice cloning, MyShell offers original text-based chatbot personalities, meme generators, user-created text RPGs, and more. Some content is locked behind a subscription fee. The company also charges bot creators to promote their bots on its platform.

By open-sourcing its voice cloning capabilities through HuggingFace while monetising its broader app ecosystem, MyShell stands to increase users across both while advancing an open model of AI development.

(Photo by Claus Grünstäudl on Unsplash)

See also: AI & Big Data Expo: Maximising value from real-time data streams

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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The post MyShell releases OpenVoice voice cloning AI appeared first on AI News.

“As 2023 draws to a close with breathless predictions about the future of artificial intelligence, some may wonder whether judges are about to become obsolete. I am sure we are not—but equally confident that technological changes will continue to transform our work,” stated Roberts.

Roberts stressed the intrinsic value of human judgement, asserting that machines could not fully replace the nuanced decisions made by individuals.

In his report, Roberts pointed out the importance of subtle factors such as a trembling hand, a momentary hesitation, or a fleeting break in eye contact—aspects that machines might struggle to discern accurately. The Chief Justice underlined the public’s inherent trust in human judgement over AI when it comes to evaluating such nuances.

However, Roberts expressed legitimate concerns about the potential drawbacks of AI in the legal domain. He warned against the possibility of AI-generated fabricated answers or “hallucinations,” citing instances where lawyers used AI-powered applications to submit briefs that referenced imaginary cases.

Additionally, Roberts highlighted the risks associated with AI influencing privacy and the potential for bias in decisions in discretionary matters like flight risk and recidivism.

Despite these apprehensions, Roberts acknowledged the positive aspects of incorporating AI in the legal system. He recognised AI’s potential to democratise access to legal advice and tools, particularly benefiting those who cannot afford legal representation.

As the legal world adapts to AI, Chief Justice Roberts’ reflections underscore the importance of striking a balance between harnessing its substantial benefits while managing the potentially devastating risks.

(Image Credit: DOD photo by Navy Petty Officer 1st Class Carlos M. Vazquez II under CC BY 2.0 DEED license)

See also: AI & Big Data Expo: Ethical AI integration and future trends

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post US Chief Justice: AI won’t replace judges but will ‘transform our work’ appeared first on AI News.

The AI & Big Data Expo stood out for its great mixture of speakers, not only targeting people working within data, but making the topics feel completely accessible to somebody like me, who isn’t a data scientist by background. As the CEO of an infrastructure charity, I know our beneficiaries don’t necessarily work closely with data, or hold it at the forefront of their minds, so it was very interesting to see how AI and big data impacts a diverse range of different sectors, and how they employ and deploy different strategies to work with its new challenges. 

I especially enjoyed the talks focused on ethics and governance, which resonate with our beneficiaries and the challenges that they face. What’s interesting is that there seems to be a real drive to ensure that ethics is baked into AI strategies moving forward. It’s very heartening that ethics is being talked about at this early stage – that it isn’t being ignored as it may have been when past technologies developed as quickly.

One talk tackled governance, and how governments are still playing catch up. There seems to be an overarching feeling that AI has to be regulated, but whether the regulation that people want is possible is the next big question. Can it be regulated, and how? Is the EU act going to work well, and is the legislation in the US going to be effective, or will it be watered down? What systems do you use, and therefore what do you endorse? Is this the right thing to do, is this the right way to deploy this sort of power, and what would the fallout be if we did?

As a charity working to serve other charities, safeguarding is a huge area of concern for us, so it’s good to know that the mainstream is also thinking about transparency. AI providers and tools have not yet done enough to flag the potential risks for third sector organisations that, for example, routinely handle sensitive data about vulnerable individuals. This could result in a number of issues for charities using AI for the first time, not least data breaches. We have already seen the misuse of AI to replace services that are still necessarily led by humans – in one example, a chatbot that replaced a manned helpline gave people with eating disorders dangerous dieting advice. Tech leaders and governments must take the lead by demonstrating responsible approaches and creating frameworks around safeguarding and risks. There will always be bad actors in this space, but there seems to be a ‘coalition of the willing’ that wants to ensure AI is continually safe, not just for those with enough resources to create their own safeguarding.

As these debates continue and the technology develops apace, it’s so important that there are spaces in which the third sector can be heard alongside private or statutory organisations. At the AI & Big Data Expo, we were able to showcase our work as a representative voice, and garner enthusiasm in the ‘data for good’ movement. We made some fantastic connections with others, as a result of realising how aligned our overarching missions are. Testament to that was the enthusiasm of our audience, asking our wonderful volunteers Adam and Alvaro tons of questions, and chatting to us in person afterwards. We are thrilled to have been part of these conversations.

Finally, we want to say a big thank you to the organisers for the opportunity to get stuck into a cross-sector event like this. We’re looking forward to the next one!

To find out more about DataKind UK and how you can support our vision of a strong, thriving third sector that embraces data science to become more impactful, visit our website.

The post Ethics, governance and data for good at the AI & Big Data Expo appeared first on AI News.

Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular.

At Canon, Zheng’s team focuses on ethically integrating AI into business by first mapping current and potential AI applications across areas like marketing and e-commerce. They then analyse and assess risks to ensure compliance with regulations.

Canon is actively mapping out AI applications and assessing risks, as Grace explained, “to align with regulations such as the EU legislations.”

As founder of Kosh Duo, Zheng also provides coaching to help businesses scale up through the use of AI marketing and data-driven approaches. She coaches professionals on achieving greater recognition and rewards by leveraging AI tools as well.

A key challenge she encounters is misunderstandings around what AI truly means – many conflate it solely with chatbots like ChatGPT rather than appreciating the full breadth of machine learning, neural networks, natural language processing, and more that enable today’s AI.

“There’s a lot of misconceptions, definitely. One of the biggest fears, as I touched on, is the very generic understanding that GPT equals AI,” says Zheng. “[Kosh Duo] provides coaching services to businesses to scale to the next level using AI marketing and data-driven approaches.”

When asked about trends to watch, Zheng emphasised the need for continual learning given how rapidly the field evolves. She expects that 2024 will be an “awakening year” where businesses truly grasp AI’s potential and individuals appreciate the need to evaluate their current skillsets.

The interview highlighted the transformative but often misunderstood power of AI in business and the importance of developing specialised skills to properly harness it. Zheng stressed that with the right ethical foundations and coaching, AI and machine learning can become positive forces to drive growth rather than something to fear.

Watch our full interview with Grace Zheng below:

(Photo by Benjamin Davies on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post AI & Big Data Expo: Ethical AI integration and future trends appeared first on AI News.

The adoption of AI is rapidly gaining momentum, with 35% of businesses currently utilizing AI-powered solutions. This trend is mirrored in the wider world, where 77% of devices in use feature some form of AI. Recognizing the competitive advantage that AI can bring, 9 out of 10 organizations support the adoption of AI. The future looks bright for AI, with projections estimating that AI will contribute a staggering $15.7 trillion to the global economy by 2030.

How can we use AI in marketing?

Incorporating AI into marketing involves leveraging the transformative power of technology to enhance various aspects of the marketing landscape. AI redefines how businesses connect with their audiences, from predictive analytics and personalized customer experience to automated decision-making and tailored messaging. This section delves into the diverse ways AI can seamlessly integrate into marketing strategies for achieving optimal results.

Best 10 AI marketing campaign examples in 2023

As artificial intelligence (AI) revolutionizes the marketing landscape, businesses are embracing its transformative power to drive effective marketing strategies and enhance digital marketing efforts. Dive into this exclusive showcase of 10 groundbreaking AI marketing campaigns that highlight the immense potential of AI to connect with audiences, boost engagement, and achieve business objectives. These case studies demonstrate how artificial intelligence can be seamlessly integrated into various marketing channels, propelling businesses toward success.

1. Heinz: AI-Generated Ketchup

Heinz, a renowned condiment brand, embraced the generative AI craze by asking a simple question: “What does AI think ketchup looks like?” They turned this question into a viral marketing campaign, using the AI image generation tool DALLE-2 to create fun and engaging images of ketchup in various settings. To further engage their audience, Heinz encouraged social sharing, allowing users to create their own AI-generated social media posts with ketchup images.

2. Ben & Jerry’s: Breakfast Flavors Discovery

Ben & Jerry’s, a leading ice cream brand, partnered with Unilever’s AI-powered trend analysis to uncover a gap in the market for breakfast-inspired ice cream flavors. Based on this data-driven insight, Ben & Jerry’s launched their “ice cream for breakfast” campaign, introducing three new flavors that resonated with consumers.

3. Shopify: AI-Powered Shopping Assistant

Shopify, a leading e-commerce platform, introduced an AI-powered shopping assistant chatbot to enhance the user experience. This chatbot can guide users through the vast selection of stores on Shopify, offering personalized recommendations and assistance.

4. Coca-Cola: Create Real Magic

Coca-Cola, a beverage giant, launched a creative AI-powered contest called “Create Real Magic.” This contest invited digital artists to create art pieces using an AI-powered web app, with the top entries featured on Coca-Cola’s digital billboards in Time Square. This collaborative campaign showcased the power of AI to foster creativity and engagement.

5. Nutella: Unica Limited Edition Labels

Nutella, a popular hazelnut spread, sought to stand out with unique and personalized packaging. Working with Ogilvy Italia, Nutella leveraged AI to create 7 million one-of-a-kind labels, each reflecting the diverse and expressive Italian culture. This campaign demonstrated the power of using AI in marketing to enhance product differentiation and brand appeal.

6. Volkswagen: AI-Powered Ad Buying

Volkswagen, a renowned automotive manufacturer, adopted a groundbreaking approach to optimize its ad-buying decisions. By leveraging AI, Volkswagen implemented an automated system that analyzes real-time data to identify the most effective ad placements and target audiences. This data-driven approach, 

artificial intelligence in marketing enabled Volkswagen to reduce its ad spend by 20% while increasing sales.

7. Netflix: Personalized Recommendations

Netflix, a leading streaming service, revolutionized the way users discover content by employing AI-powered personalization algorithms. These algorithms analyze user behavior and preferences to recommend relevant movies, TV shows, and даже artwork that aligns with individual tastes.

7. Mastercard: Crayon AI for Competitive Intelligence

Mastercard, a global payments company, faced the challenge of staying ahead of the curve in an ever-evolving competitive landscape. To gain a deeper understanding of competitor strategies and anticipate market trends, Mastercard integrated AI into their business operations. By employing Crayon, an AI-powered competitive intelligence platform, Mastercard gained a clear picture of its competitors’ activities and market movements. This insights-driven approach empowered Mastercard to make informed business decisions and protect its market position.

8. Nike: Immersive AI-Generated Match

Nike, a sportswear brand, collaborated with Serena Williams to create an AI-generated match that juxtaposed her younger self from 1999 against her modern self from 2017. This award-winning campaign commemorated Nike’s 50th anniversary and showcased the emotional connection that AI can evoke.

9. BMW: Projecting AI Art onto Cars 

A luxury car manufacturer, BMW partnered with an advertising agency to project AI-generated art onto their 8 Series Gran Coupé. This innovative campaign aimed to connect with BMW’s target audience on an emotional level and highlight the brand’s creativity and innovation.

10. Calm App: Personalized Content Recommendations

Calm, a popular meditation app, leveraged Amazon Personalize, an AI-powered product recommendation engine, to provide personalized content recommendations to users. This dynamic approach ensures that users are presented with relevant content that aligns with their preferences, enhancing the overall user experience.

These ten examples demonstrate the transformative power of AI in marketing, highlighting its ability to drive innovation, personalization, and data-driven decision-making. As AI continues to evolve, its impact on marketing is expected to grow even more profound, paving the way for even more creative and effective marketing campaigns.

In conclusion

From Heinz’s creative use of AI-generated images to Ben & Jerry’s data-driven flavor launch, these examples showcase how AI can be applied to shape personalized customer experiences, optimize ad targeting, and personalize content recommendations. By harnessing the power of AI, business can elevate their AI marketing strategy to unprecedented heights, achieving deeper customer engagement, enhanced conversion rates, and a competitive edge in the ever-evolving digital marketplace.

To further explore the role of AI in marketing and witness groundbreaking campaigns, consider reaching out to a leading AI marketing agency. With their expertise and guidance, you can unlock the full potential of AI and transform your marketing campaigns into resounding successes.

To conclude, the era of AI in marketing presents a world of unprecedented opportunities to transform data into gold. By embracing AI-based marketing campaigns, businesses can not only stay ahead of the competition but also revolutionize how they connect with their audience, paving the way for a future where data truly becomes a valuable asset in the marketing realm.

(Editor’s note: This article is sponsored by Digital Agency Network)

The post Turning data into gold: 10 exceptional AI marketing campaign examples appeared first on AI News.

During the discussion, Grande provided insightful perspectives on the current challenges, how Edge Impulse is helping address these struggles, and the tremendous promise of on-device AI.

Key hurdles with edge AI adoption

Grande highlighted three primary pain points companies face when attempting to productise edge machine learning models, including difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.

“A lot of the companies building edge devices are not very familiar with machine learning,” says Grande. “Bringing those two worlds together is the third challenge, really, around having teams communicate with each other and being able to share knowledge and work towards the same goals.”

Strategies for lean and efficient models

When asked how to optimise for edge environments, Grande emphasised first minimising required sensor data.

“We are seeing a lot of companies struggle with the dataset. What data is enough, what data should they collect, what data from which sensors should they collect the data from. And that’s a big struggle,” explains Grande.

Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy. Always balance sensor and hardware constraints against functionality, connectivity needs, and software requirements.

Edge Impulse aims to enable engineers to validate and verify models themselves pre-deployment using common ML evaluation metrics, ensuring reliability while accelerating time-to-value. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms.

Transformative potential of on-device intelligence

Grande highlighted innovative products already leveraging edge intelligence to provide personalised health insights without reliance on the cloud, such as sleep tracking with Oura Ring.

“It’s sold over a billion pieces, and it’s something that everybody can experience and everybody can get a sense of really the power of edge AI,” explains Grande.

Other exciting opportunities exist around preventative industrial maintenance via anomaly detection on production lines.

Ultimately, Grande sees massive potential for on-device AI to greatly enhance utility and usability in daily life. Rather than just raw data, edge devices can interpret sensor inputs to provide actionable suggestions and responsive experiences not previously possible—heralding more useful technology and improved quality of life.

Unlocking the potential of AI on edge devices hinges on overcoming current obstacles inhibiting adoption. Grande and other leading experts provided deep insights at this year’s AI & Big Data Expo on how to break down the barriers and unleash the full possibilities of edge AI.

“I’d love to see a world where the devices that we were dealing with were actually more useful to us,” concludes Grande.

Watch our full interview with Alessandro Grande below:

(Photo by Niranjan _ Photographs on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post AI & Big Data Expo: Unlocking the potential of AI on edge devices appeared first on AI News.

Available for Vertex AI customers on the allowlist, Imagen 2 enables users to craft and deploy photorealistic images using intuitive tooling and fully-managed infrastructure. 

Developed with Google DeepMind technology, Imagen 2 offers improved image quality and a range of functionalities tailored for specific use cases.

Key features of Imagen 2 include:

• Diverse image generation: Imagen 2 excels in creating high-resolution images from natural language prompts that cater to various user requirements.

• Text rendering in multiple languages: Overcoming common challenges, Imagen 2 supports accurate text rendering in multiple languages.

• Logo generation: Businesses can leverage Imagen 2 to create a variety of creative and realistic logos—with the option to overlay them on products, clothing, business cards, and more.

• Captions and question-answering: Imagen 2’s advanced image understanding capabilities facilitate the creation of descriptive captions and provide detailed answers to questions about image elements.

• Multi-language support: Imagen 2 introduces support for six additional languages in preview, with plans for more in early 2024. This includes the ability to translate between prompt and output.

• Safety measures: Imagen 2 incorporates built-in safety precautions, aligning with Google’s Responsible AI principles. It features safety filters and integrates with a digital watermarking service to ensure responsible use.

Enterprise-ready capabilities

Imagen 2 on Vertex AI is designed to meet enterprise standards, offering reliability and governance akin to its predecessor. With new features such as high-quality image rendering, improved text rendering, logo generation, and safety measures, Imagen 2 aims to provide organisations with a comprehensive tool for creative image generation.

Leading companies like Snap, Shutterstock, and Canva have already embraced Imagen for creative purposes.

Chris Loy, Director of AI Services at Shutterstock, commented: “We exist to empower the world to tell their stories by bridging the gap between idea and execution.

“Variety is critical for the creative process, which is why we continue to integrate the latest and greatest technology into our image generator and editing features—as long as it is built on responsibly sourced data,”

Danny Wu, Head of AI at Canva, added: “We’re continuing to use generative AI to innovate the design process and augment imagination.

“With Imagen, our 170M+ monthly users can benefit from the image quality improvements to uplevel their content creation at scale.”

As Imagen 2 makes waves in the creative industry, organisations are encouraged to explore its potential. Google Cloud anticipates users will harness the new features to elevate their creative endeavours and build on the success achieved with Imagen.

(Photo by G on Unsplash)

See also: Microsoft unveils 2.7B parameter language model Phi-2

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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The post Google Cloud announces Imagen 2 text-to-image generator appeared first on AI News.

Phi-2 builds upon the success of its predecessors, Phi-1 and Phi-1.5, by matching or surpassing models up to 25 times larger—thanks to innovations in model scaling and training data curation.

The compact size of Phi-2 makes it an ideal playground for researchers, facilitating exploration in mechanistic interpretability, safety improvements, and fine-tuning experimentation across various tasks.

Phi-2’s achievements are underpinned by two key aspects:

• Training data quality: Microsoft emphasises the critical role of training data quality in model performance. Phi-2 leverages “textbook-quality” data, focusing on synthetic datasets designed to impart common sense reasoning and general knowledge. The training corpus is augmented with carefully selected web data, filtered based on educational value and content quality.

• Innovative scaling techniques: Microsoft adopts innovative techniques to scale up Phi-2 from its predecessor, Phi-1.5. Knowledge transfer from the 1.3 billion parameter model accelerates training convergence, leading to a clear boost in benchmark scores.

Performance evaluation

Phi-2 has undergone rigorous evaluation across various benchmarks, including Big Bench Hard, commonsense reasoning, language understanding, math, and coding.

With only 2.7 billion parameters, Phi-2 outperforms larger models – including Mistral and Llama-2 – and matches or outperforms Google’s recently-announced Gemini Nano 2:

Beyond benchmarks, Phi-2 showcases its capabilities in real-world scenarios. Tests involving prompts commonly used in the research community reveal Phi-2’s prowess in solving physics problems and correcting student mistakes, showcasing its versatility beyond standard evaluations:

Phi-2 is a Transformer-based model with a next-word prediction objective, trained on 1.4 trillion tokens from synthetic and web datasets. The training process – conducted on 96 A100 GPUs over 14 days – focuses on maintaining a high level of safety and claims to surpass open-source models in terms of toxicity and bias.

With the announcement of Phi-2, Microsoft continues to push the boundaries of what smaller base language models can achieve.

(Image Credit: Microsoft)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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The post Microsoft unveils 2.7B parameter language model Phi-2 appeared first on AI News.

The report underscores the need for a composite AI approach. This involves combining various AI types – such as generative, predictive, and causal – along with diverse data sources like observability, security, and business events. This holistic strategy aims to provide precision, context, and meaning to AI outputs, ensuring reliable results.

Key findings:

• 83% of tech leaders emphasise the mandatory role of AI in navigating the dynamic nature of cloud environments.
• 82% anticipate AI’s critical role in security threat detection, investigation, and response.
• 88% foresee AI extending access to data analytics for non-technical employees through natural language queries.
• 88% believe AI will enhance cloud cost efficiencies through support for Financial Operations (FinOps) practices.

“AI has become central to how organisations drive efficiency, improve productivity, and accelerate innovation,” said Bernd Greifeneder, Chief Technology Officer at Dynatrace.

“The release of ChatGPT late last year triggered a significant generative AI hype cycle. Business, development, operations, and security leaders have set high expectations for generative AIs to help them deliver new services with less effort and at record speeds.”

While organisations express optimism about AI’s transformative potential, concerns linger:

• 93% of tech leaders worry about potential non-approved uses of AI as employees become more accustomed to tools like ChatGPT.
• 95% express concerns about using generative AI for code generation, fearing leakage and improper use of intellectual property.
• 98% are apprehensive about unintentional bias, errors, and misinformation in generative AI.

“Especially for use cases that involve automation and depend on data context, taking a composite approach to AI is critical. For instance, automating software services, resolving security vulnerabilities, predicting maintenance needs, and analysing business data all need a composite AI approach,” added Greifeneder.

“This approach should deliver the precision of causal AI, which determines the underlying causes and effects of systems’ behaviours, and predictive AI, which forecasts future events based on historical data.”

As organisations forge ahead with AI adoption, balancing enthusiasm with a mindful approach to challenges becomes paramount. The survey underscores the transformative potential of AI, but its effective integration requires careful consideration and a diversified AI strategy.

“Predictive AI and causal AI not only provide essential context for responses produced by generative AI but can also prompt generative AI to ensure precise, non-probabilistic answers are embedded into its response,” says Greifeneder.

“If organisations get their strategy right, combining these different types of AI with high-quality observability, security, and business events data can significantly boost the productivity of their development, operations, and security teams and deliver lasting business value.”

A full copy of the report can be found here (registration required)

(Photo by Matt Sclarandis on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Dynatrace: Organisations embrace AI, yet face challenges appeared first on AI News.

Titled “A Framework for U.S. AI Governance: Creating a Safe and Thriving AI Sector,” the main policy paper proposes leveraging current US government entities to regulate AI tools within their respective domains.

Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, emphasises the pragmatic approach of initially focusing on areas where human activity is already regulated and gradually expanding to address emerging risks associated with AI.

The framework underscores the importance of defining the purpose of AI tools, aligning regulations with specific applications and holding AI providers accountable for the intended use of their technologies.

Asu Ozdaglar, deputy dean of academics in the MIT Schwarzman College of Computing, believes having AI providers articulate the purpose and intent of their tools is crucial for determining liability in case of misuse.

Addressing the complexity of AI systems existing at multiple levels, the brief acknowledges the challenges of governing both general and specific AI tools. The proposal advocates for a self-regulatory organisation (SRO) structure to supplement existing agencies, offering responsive and flexible oversight tailored to the rapidly evolving AI landscape.

Furthermore, the policy papers call for advancements in auditing AI tools—exploring various pathways such as government-initiated, user-driven, or legal liability proceedings.

The consideration of a government-approved SRO – akin to the Financial Industry Regulatory Authority (FINRA) – is proposed to enhance domain-specific knowledge and facilitate practical engagement with the dynamic AI industry.

MIT’s involvement in AI governance stems from its recognised expertise in AI research, positioning the institution as a key contributor to addressing the challenges posed by evolving AI technologies. The release of these whitepapers signals MIT’s commitment to promoting responsible AI development and usage.

You can find MIT’s series of AI policy briefs here.

(Photo by Aaron Burden on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post MIT publishes white papers to guide AI governance appeared first on AI News.