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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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

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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

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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)

See also: AI & Big Data Expo: AI’s impact on decision-making in marketing

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The guidelines were developed by the UK’s National Cyber Security Centre (NCSC) and the US’ Cybersecurity and Infrastructure Security Agency (CISA). They have already secured endorsements from 17 other countries, including all G7 members.

The guidelines provide recommendations for developers and organisations using AI to incorporate cybersecurity at every stage. This “secure by design” approach advises baking in security from the initial design phase through development, deployment, and ongoing operations.  

Specific guidelines cover four key areas: secure design, secure development, secure deployment, and secure operation and maintenance. They suggest security behaviours and best practices for each phase.

The launch event in London convened over 100 industry, government, and international partners. Speakers included reps from Microsoft, the Alan Turing Institute, and cyber agencies from the US, Canada, Germany, and the UK.  

NCSC CEO Lindy Cameron stressed the need for proactive security amidst AI’s rapid pace of development. She said, “security is not a postscript to development but a core requirement throughout.”

The guidelines build on existing UK leadership in AI safety. Last month, the UK hosted the first international summit on AI safety at Bletchley Park.

US Secretary of Homeland Security Alejandro Mayorkas said: “We are at an inflection point in the development of artificial intelligence, which may well be the most consequential technology of our time. Cybersecurity is key to building AI systems that are safe, secure, and trustworthy.

“The guidelines jointly issued today by CISA, NCSC, and our other international partners, provide a common-sense path to designing, developing, deploying, and operating AI with cybersecurity at its core.”

The 18 endorsing countries span Europe, Asia-Pacific, Africa, and the Americas. Here is the full list of international signatories:

• Australia – Australian Signals Directorate’s Australian Cyber Security Centre (ACSC)
• Canada – Canadian Centre for Cyber Security (CCCS) 
• Chile – Chile’s Government CSIRT
• Czechia – Czechia’s National Cyber and Information Security Agency (NUKIB)
• Estonia – Information System Authority of Estonia (RIA) and National Cyber Security Centre of Estonia (NCSC-EE)
• France – French Cybersecurity Agency (ANSSI)
• Germany – Germany’s Federal Office for Information Security (BSI)
• Israel – Israeli National Cyber Directorate (INCD)
• Italy – Italian National Cybersecurity Agency (ACN)
• Japan – Japan’s National Center of Incident Readiness and Strategy for Cybersecurity (NISC; Japan’s Secretariat of Science, Technology and Innovation Policy, Cabinet Office
• New Zealand – New Zealand National Cyber Security Centre
• Nigeria – Nigeria’s National Information Technology Development Agency (NITDA)
• Norway – Norwegian National Cyber Security Centre (NCSC-NO)
• Poland – Poland’s NASK National Research Institute (NASK)
• Republic of Korea – Republic of Korea National Intelligence Service (NIS)
• Singapore – Cyber Security Agency of Singapore (CSA)
• United Kingdom – National Cyber Security Centre (NCSC)
• United States of America – Cybersecurity and Infrastructure Agency (CISA); National Security Agency (NSA; Federal Bureau of Investigations (FBI)

UK Science and Technology Secretary Michelle Donelan positioned the new guidelines as cementing the UK’s role as “an international standard bearer on the safe use of AI.”

“Just weeks after we brought world leaders together at Bletchley Park to reach the first international agreement on safe and responsible AI, we are once again uniting nations and companies in this truly global effort,” adds Donelan.

The guidelines are now published on the NCSC website alongside explanatory blogs. Developer uptake will be key to translating the secure by design vision into real-world improvements in AI security.

(Photo by Jan Antonin Kolar on Unsplash)

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

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Inflection-2 was trained on over 5,000 NVIDIA GPUs to reach 1.025 quadrillion floating point operations (FLOPs), putting it in the same league as PaLM 2 Large. However, early benchmarks show Inflection-2 outperforming Google’s model on tests of reasoning ability, factual knowledge, and stylistic prowess.

On a range of common academic AI benchmarks, Inflection-2 achieved higher scores than PaLM 2 on most. This included outscoring the search giant’s flagship on the diverse Multi-task Middle-school Language Understanding (MMLU) tests, as well as TriviaQA, HellaSwag, and the Grade School Math (GSM8k) benchmarks:

The startup’s new model will soon power its personal assistant app Pi to enable more natural conversations and useful features.

Inflection said its transition from NVIDIA A100 to H100 GPUs for inference – combined with optimisation work – will increase serving speed and reduce costs despite Inflection-2 being much larger than its predecessor.  

An Inflection spokesperson said this latest model brings them “a big milestone closer” towards fulfilling the mission of providing AI assistants for all. They added the team is “already looking forward” to training even larger models on their 22,000 GPU supercluster.

Safety is said to be a top priority for the researchers, with Inflection being one of the first signatories to the White House’s July 2023 voluntary AI commitments. The company said its safety team continues working to ensure models are rigorously evaluated and rely on best practices for alignment.

With impressive benchmarks and plans to scale further, Inflection’s latest effort poses a serious challenge to tech giants like Google and Microsoft who have so far dominated the field of large language models. The race is on to deliver the next generation of AI.

(Photo by Johann Walter Bantz on Unsplash)

See also: Anthropic upsizes Claude 2.1 to 200K tokens, nearly doubling GPT-4

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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

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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Altman, who was recently ousted by OpenAI’s board for reasons that have had no shortage of speculation, has found a new home at Microsoft. The announcement came after unsuccessful negotiations with OpenAI’s board to reinstate Altman.

Microsoft CEO Satya Nadella – who has long expressed confidence in Altman’s vision and leadership – revealed that Altman and Brockman will lead Microsoft’s newly established advanced AI research team.

Nadella expressed excitement about the collaboration, stating, “We’re extremely excited to share the news that Sam Altman and Greg Brockman, together with colleagues, will be joining Microsoft to lead a new advanced AI research team. We look forward to moving quickly to provide them with the resources needed for their success.”

The move follows Altman’s abrupt departure from OpenAI. Former Twitch CEO Emmett Shear has been appointed as interim CEO at OpenAI.

Altman’s role at Microsoft is anticipated to build on the company’s strategy of allowing founders and innovators space to create independent identities, similar to Microsoft’s approach with GitHub, Mojang Studios, and LinkedIn.

Microsoft’s decision to bring Altman and Brockman on board coincides with the development of its custom AI chip. The Maia AI chip, designed to train large language models, aims to reduce dependence on Nvidia.

While Microsoft reassures its commitment to the OpenAI partnership, valued at approximately $10 billion, it emphasises ongoing innovation and support for customers and partners.

As Altman and Brockman embark on leading Microsoft’s advanced AI research team, the industry will be watching closely to see what the high-profile figures can do with Microsoft’s resources at their disposal. The industry will also be observing whether OpenAI can maintain its success under different leadership.

(Photo by Turag Photography on Unsplash)

See also: Amdocs, NVIDIA and Microsoft Azure build custom LLMs for telcos

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GlobalData highlights a 14 percent compound annual growth rate (CAGR) increase in related patent filings from 2020 to 2022, underscoring the vast influence and potential of quantum AI across industries.

Adarsh Jain, Director of Financial Markets at GlobalData, emphasises the transformative nature of Quantum AI:

“Quantum AI represents a transformative advancement in technology. As we integrate quantum principles into AI algorithms, the potential for speed and efficiency in processing complex data sets grows exponentially. This not only enhances current AI applications but also opens new possibilities across various industries. 

The surge in patent filings is a testament to its growing importance and the pivotal role it will play in the future of AI-driven solutions.”

Kiran Raj, Practice Head of Disruptive Tech at GlobalData, highlights that while AI thrives on data and computational power, the inner workings of the technology often remain unclear. Quantum computing not only promises increased power but also potentially provides greater insights into these workings, paving the way for AI to transcend its current capabilities.

GlobalData’s Disruptor Intelligence Center analysis reveals significant synergy between quantum computing and AI innovations, leading to revolutionary impacts in various industries. Notable collaborations include HSBC and IBM in finance, Menten AI’s healthcare advancements, Volkswagen’s partnership with Xanadu for battery simulation, Intel’s Quantum SDK, and Zapata’s collaboration with BMW.

Raj concludes with a note of caution: “Quantum AI offers the potential for smarter, faster AI systems, but its adoption is complex and demands caution. The technology is still in its early stages, requiring significant investment and expertise.

“Key challenges include the need for advanced cybersecurity measures and ensuring ethical AI practices as we navigate this promising yet intricate landscape.”

(Photo by Anton Maksimov 5642.su on Unsplash)

See also: Google expands partnership with Anthropic to enhance AI safety

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The recent GitLab 16.6 November release includes the beta launch of GitLab Duo Chat, a natural-language AI assistant. Additionally, the GitLab 16.7 December release sees the general availability of GitLab Duo Code Suggestions.

David DeSanto, Chief Product Officer at GitLab, said: “To realise AI’s full potential, it needs to be embedded across the software development lifecycle, allowing DevSecOps teams to benefit from boosts to security, efficiency, and collaboration.”

GitLab Duo Chat – arguably the star of the show – provides users with invaluable insights, guidance, and suggestions. Beyond code analysis, it supports planning, security issue comprehension and resolution, troubleshooting CI/CD pipeline failures, aiding in merge requests, and more.

As part of GitLab’s commitment to providing a comprehensive AI-powered experience, Duo Chat joins Code Suggestions as the primary interface into GitLab’s AI suite within its DevSecOps platform.

GitLab Duo comprises a suite of 14 AI capabilities:

• Suggested Reviewers
• Code Suggestions
• Chat
• Vulnerability Summary
• Code Explanation
• Planning Discussions Summary
• Merge Request Summary
• Merge Request Template Population
• Code Review Summary
• Test Generation
• Git Suggestions
• Root Cause Analysis
• Planning Description Generation
• Value Stream Forecasting

In response to the evolving needs of development, security, and operations teams, Code Suggestions is now generally available. This feature assists in creating and updating code, reducing cognitive load, enhancing efficiency, and accelerating secure software development.

GitLab’s commitment to privacy and transparency stands out in the AI space. According to the GitLab report, 83 percent of DevSecOps professionals consider implementing AI in their processes essential, with 95 percent prioritising privacy and intellectual property protection in AI tool selection.

The State of AI in Software Development report by GitLab reveals that developers spend just 25 percent of their time writing code. The Duo suite aims to address this by reducing toolchain sprawl—enabling 7x faster cycle times, heightened developer productivity, and reduced software spend.

Kate Holterhoff, Industry Analyst at Redmonk, commented: “The developers we speak with at RedMonk are keenly interested in the productivity and efficiency gains that code assistants promise.

“GitLab’s Duo Code Suggestions is a welcome player in this space, expanding the available options for enabling an AI-enhanced software development lifecycle.”

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See also: OpenAI battles DDoS against its API and ChatGPT services

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