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)

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“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

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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)

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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

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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

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For a long time, there has been a lack of synchronisation between lip movements and voices in dubbed content. Experts believe this is one of the reasons why dubbing is relatively unpopular in English-speaking countries. In fact, lip movements make localised content more realistic and therefore more appealing to audiences.

There is a study by Yukari Hirata, a professor known for her work in linguistics, which says that watching lip movements (rather than gestures) helps to perceive difficult phonemic contrasts in the second language. Lip reading is also one of the ways we learn to speak in general.   

Today, with Rask’s new feature, it’s possible to take localised content to a new level, making dubbed videos more natural.

The AI automatically restructures the lower face based on references. It takes into account how the speaker looks and what they are saying to make the end result more realistic. 

How it works:

1. Upload a video with one or more people in the frame.
2. Translate the video into another language.
3. Press the ‘Lip Sync Check’ button and the algorithm will evaluate the video for lip sync compatibility.
4. If the video passes the check, press ‘Lip Sync’ and wait for the result.
5. Download the video.

According to Maria Chmir, founder and CEO of Rask AI, the new feature will help content creators expand their audience. The AI visually adjusts lip movements to make a character appear to speak the language as fluently as a native speaker. 

The technology is based on generative adversarial network (GAN) learning, which consists of a generator and a discriminator. Both the generator and the discriminator compete with each other to stay one step ahead of the other. The generator clearly generates content (lip movements), while the discriminator is responsible for quality control.     

The beta release is available to all Rask subscription customers.

(Editor’s note: This article is sponsored by Rask AI)

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Demis Hassabis, CEO and Co-Founder of Google DeepMind, introduced Gemini as a multimodal model that is capable of seamlessly understanding and combining various types of information, including text, code, audio, image, and video.

Gemini comes in three optimised versions: Ultra, Pro, and Nano. The Ultra model boasts state-of-the-art performance, surpassing human experts in language understanding and demonstrating unprecedented capabilities in tasks ranging from coding to multimodal benchmarks.

What sets Gemini apart is its native multimodality, eliminating the need for stitching together separate components for different modalities. This groundbreaking approach, fine-tuned through large-scale collaborative efforts across Google teams, positions Gemini as a flexible and efficient model capable of running on data centres to mobile devices.

One of Gemini’s standout features is its sophisticated multimodal reasoning, enabling it to extract insights from vast datasets with remarkable precision. The model’s prowess extends to understanding and generating high-quality code in popular programming languages.

However, as Google ventures into this new era of AI, responsibility and safety remain paramount. Gemini undergoes rigorous safety evaluations, including assessments for bias and toxicity. Google is actively collaborating with external experts to address potential blind spots and ensure the model’s ethical deployment.

Gemini 1.0 is now rolling out across various Google products – including the Bard chatbot – with plans for integration into Search, Ads, Chrome, and Duet AI. However, the Bard upgrade will not be released in Europe pending clearance from regulators.

Developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI. Android developers will also be able to build with Gemini Nano via AICore, a new system capability available in Android 14.

(Image Credit: Google)

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This collaboration will capitalise on Absci’s Integrated Drug Creation platform—seamlessly integrating with AstraZeneca’s expertise in oncology, aiming to expedite the discovery of a potentially game-changing cancer therapy.

Under the agreement, Absci will deploy its pioneering generative AI technology to craft a therapeutic candidate antibody tailored for a specific oncology target. The collaboration encompasses an upfront commitment, substantial R&D funding, milestone payments, and royalties on future product sales.

Sean McClain, Founder & CEO of Absci, said: “AstraZeneca is a leader in developing novel treatments in oncology, and we are excited to collaborate with them to design a therapeutic candidate antibody with the potential to improve the lives of cancer patients.”

Absci’s Integrated Drug Creation platform combines generative AI and scalable wet-lab technologies, generating proprietary data by scrutinising millions of protein-protein interactions. This data fuels Absci’s proprietary AI models, facilitating the design of antibodies that are later validated through wet-lab experiments.

This accelerated approach, completing the entire cycle within approximately six weeks, enhances the probability of successful development outcomes for biologic drug candidates.

Puja Sapra, PhD, SVP of Biologics Engineering & Oncology Targeted Delivery at AstraZeneca, commented: “This collaboration is an exciting opportunity to utilise Absci’s de novo AI antibody creation platform to design a potential new antibody therapy in oncology.”

The announcement follows Absci’s recent publication on the design and validation of de novo antibodies using their state-of-the-art ‘zero-shot’ generative AI model.

The collaboration between Absci and AstraZeneca should further help to demonstrate how AI can be used to revolutionise drug discovery.

(Photo by National Cancer Institute on Unsplash)

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The focal point of Smith’s presentation was a strategic experiment conducted by his team to explore the potential of AI in reducing noise and bias inherent in decision-making. Smith began by addressing the common perception of decision-making and the often-overlooked influence of human biases and external factors.

“Let’s recognise that we’re not the best at making decisions, that there are some issues when we make decisions—just as there are some issues when AI makes some decisions,” said Smith.

Smith advocates for combining the strengths of both human and AI decision-makers.

The strategic experiment involved a comprehensive analysis of the human decision-making process, where the team pitted AI against a human team in running a Facebook travel campaign. Smith delved into the intricacies of the human brain’s dual components—the amygdala for intuitive thinking and the prefrontal cortex for reasoning.

Notably, Smith drew attention to the concept of “noise,” a term he differentiated from bias, describing it as the variance in decision-making that introduces inconsistencies. He supported this with examples from various professions, such as judges delivering differing sentences based on external factors.

The challenges within the marketing and advertising space were highlighted, particularly the difficulty of managing a vast number of variables—illustrated by a campaign with a staggering 83 million variations.

“There’s no way that a human can realistically go through 83 million [ad variation] combinations,” said Smith. “AI is better at picking out important signals in large data sets.”

Initially, the results of the strategic experiment showed humans outperforming the AI-optimised campaign, However, the AI campaign quickly pulled away:

While acknowledging AI’s flaws — including bias — Smith advocated for a collaborative approach, envisioning a balance between human intuition and AI assistance. He highlighted the importance of recognising human limitations and leveraging AI to reduce decision-making flaws.

The presentation concluded with key takeaways, encouraging the recognition of human decision-making limitations, leveraging AI to reduce flaws, and finding the right balance between human input and AI assistance.

See also: Ampere Computing: Unlocking a Path to the Sustainable Cloud

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The release comes on the heels of an expanded partnership with Google that provides Anthropic access to advanced processing hardware, enabling the substantial expansion of Claude’s context-handling capabilities.

With the ability to process lengthy documents like full codebases or novels, Claude 2.1 is positioned to unlock new potential across applications from contract analysis to literary study. 

The 200K token window represents more than just an incremental improvement—early tests indicate Claude 2.1 can accurately grasp information from prompts over 50 percent longer than GPT-4 before the performance begins to degrade.

Anthropic also touted a 50 percent reduction in hallucination rates for Claude 2.1 over version 2.0. Increased accuracy could put the model in closer competition with GPT-4 in responding precisely to complex factual queries.

Additional new features include an API tool for advanced workflow integration and “system prompts” that allow users to define Claude’s tone, goals, and rules at the outset for more personalised, contextually relevant interactions. For instance, a financial analyst could direct Claude to adopt industry terminology when summarising reports.

However, the full 200K token capacity remains exclusive to paying Claude Pro subscribers for now. Free users will continue to be limited to Claude 2.0’s 100K tokens.

As the AI landscape shifts, Claude 2.1’s enhanced precision and adaptability promise to be a game changer—presenting new options for businesses exploring how to strategically leverage AI capabilities.

With its substantial context expansion and rigorous accuracy improvements, Anthropic’s latest offering signals its determination to compete head-to-head with leading models like GPT-4.

(Image Credit: Anthropic)

See also: Paul O’Sullivan, Salesforce: Transforming work in the GenAI era

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