Since its launch in May 2023, Amperity for Paid Media has used industry-leading ad connectors and first-party data to deliver more than 11 billion unified customer profiles each day. These are delivered to the ad platforms of Amperity customers, across a range of industries, including retail, quick-serve restaurants (QSR), consumer packaged goods (CPG), travel and hospitality, sports teams and leagues, and financial services.

Marketers and digital agencies continue to struggle to find a way to measure digital and in-store transactions to deliver highly personalized campaigns and optimize their budget spend – which is where Amperity excels. Brands using Amperity for paid media are experiencing the following results:

• 3X conversion rate using unified customer profile lookalike audiences over third-party audiences
• 85%+ match rate across major ad platforms
• 30% onboarding savings
• 5x increase in ROAS (Return on Ad Spend)
• 94% savings in data management and stitch processing
• 70%+ reduction in marketing timelines

The elimination of third-party cookies and the ever-changing data privacy laws have ushered in a new era of challenges and opportunities. The days of relying solely on legacy methods to identify, retain, and acquire customers are gone. To stay ahead of the game and ensure brands are getting the most out of their largest spend channels, it’s critical that brands tap into their first-party customer data to maximize every campaign dollar, especially in this macroeconomic climate.

“Today, we find ourselves at the epicentre of a marketing revolution. The tides have shifted and the old ways of acquiring and retaining customers are giving way to a new era of data privacy and consumer-centricity,” said Barry Padgett, CEO at Amperity. “In Q1 of next year, Google is going to disable 1% of third-party cookies and fully remove them by Q3. This poses a massive challenge for brands across the board. But within this challenge lies immense opportunity. At Amperity, we’ve taken it upon ourselves to lead the charge and help brands and agencies navigate this shift.”

To quantify the impact Amperity is having on paid media, the company commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential ROI enterprises may realize by deploying its CDP. To construct the TEI study, Forrester interviewed five Amperity customers, identifying the benefits, risks, and outcomes experienced while using the company’s customer data platform for paid media and combined the results to form a single composite organization. According to the study, the composite organization not only experienced a 505% ROI but also experienced the following benefits over three years:

• $3.4 million incremental increase in net operating revenue from effective messaging
• $3.8 million incremental increase in net operating revenue due to targeted paid media spend
• 25% increase in productivity impact of more efficient campaign preparation and execution resulting in $1.3 million savings
• $4.5 million savings in paid media spend from deduplicating customer records

Amperity has become the customer data platform of choice for leading brands across numerous industries, most recently selected by First Hawaiian Bank, Forever 21, Palace Resorts, Shiseido America, and Virgin Atlantic. These brands join longtime users of Amperity including Alaska Airlines, Brooks, DICK’s Sporting Goods, Reckitt, Seattle Sounders, SPARC Group, and Wyndham Hotels & Resorts.

To learn more about the ROI of Amperity for paid media, download the latest TEI study here.

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

The post Customer adoption of Amperity for paid media soars to over 50% appeared first on AI News.

The result of the collaboration is the Siemens Industrial Copilot, a powerful AI assistant designed to enhance collaboration between humans and machines in the manufacturing sector. The tool enables rapid generation, optimisation, and debugging of complex automation code, significantly reducing simulation times from weeks to minutes.

At the core of this collaboration is the integration of Siemens Industrial Copilot with Microsoft Teams, connecting design engineers, frontline workers, and various teams across business functions. This integration simplifies virtual collaboration, empowering professionals with new AI-powered tools and simplifying tasks that previously required extensive time and effort.

Empowering industries with Generative AI

Satya Nadella, Chairman and CEO of Microsoft, expressed the immense potential of this collaboration, stating: “With this next generation of AI, we have a unique opportunity to accelerate innovation across the entire industrial sector.”

Siemens CEO Roland Busch echoed this sentiment; emphasising the revolutionary impact on design, development, manufacturing, and operations.

The companies envision AI copilots becoming integral in industries such as manufacturing, infrastructure, transportation, and healthcare.

Schaeffler AG – a leading automotive supplier – is already embracing generative AI, enabling engineers to generate reliable code for industrial automation systems. Siemens Industrial Copilot will work to reduce downtimes.

Facilitating virtual collaboration

To facilitate virtual collaboration, Siemens and Microsoft are launching Teamcenter for Microsoft Teams; an application that utilises generative AI to connect functions across the product design and manufacturing lifecycle.

This integration will allow millions of workers who previously lacked access to Product Lifecycle Management (PLM) tools to contribute seamlessly to the design and manufacturing processes.

The collaboration between Microsoft and Siemens looks set to be an excellent case study of how AI empowers industries and professionals, revolutionising traditional workflows and fostering global innovation.

(Photo by Sezer Arslan on Unsplash)

See also: Bob Briski, DEPT®:  A dive into the future of AI-powered experiences

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 IoT Tech Expo and Digital Transformation Week.

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

The post Microsoft and Siemens revolutionise industry with AI-powered Copilot appeared first on AI News.

At the core of DEPT®’s approach is the strategic utilisation of large language models. Briski articulated the delicate balance between the ‘pioneering’ and ’boutique’ ethos encapsulated in their tagline, “pioneering work on a global scale with a boutique culture.”

While ‘pioneering’ and ’boutique’ evokes innovation and personalised attention, ‘global scale’ signifies the broad outreach. DEPT® harnesses large language models to disseminate highly targeted, personalised messages to expansive audiences. These models, Briski pointed out, enable DEPT® to comprehend individuals at a massive scale and foster meaningful and individualised interactions.

“The way that we have been using a lot of these large language models is really to deliver really small and targeted messages to a large audience,” says Briski.

However, the integration of AI into various domains – such as retail, sports, education, and healthcare – poses both opportunities and challenges. DEPT® navigates this complexity by leveraging generative AI and large language models trained on diverse datasets, including vast repositories like Wikipedia and the Library of Congress.

Briski emphasised the importance of marrying pre-trained data with DEPT®’s domain expertise to ensure precise contextual responses. This approach guarantees that clients receive accurate and relevant information tailored to their specific sectors.

“The pre-training of these models allows them to really expound upon a bunch of different domains,” explains Briski. “We can be pretty sure that the answer is correct and that we want to either send it back to the client or the consumer or some other system that is sitting in front of the consumer.”

One of DEPT®’s standout achievements lies in its foray into the web3 space and the metaverse. Briski shared the company’s collaboration with Roblox, a platform synonymous with interactive user experiences. DEPT®’s collaboration with Roblox revolves around empowering users to create and enjoy user-generated content at an unprecedented scale. 

DEPT®’s internal project, Prepare to Pioneer, epitomises its commitment to innovation by nurturing embryonic ideas within its ‘Greenhouse’. DEPT® hones concepts to withstand the rigours of the external world, ensuring only the most robust ideas reach their clients.

“We have this internal project called The Greenhouse where we take these seeds of ideas and try to grow them into something that’s tough enough to handle the external world,” says Briski. “The ones that do survive are much more ready to use with our clients.”

While the allure of AI-driven solutions is undeniable, Briski underscored the need for caution. He warns that AI is not inherently transparent and trustworthy and emphasises the imperative of constructing robust foundations for quality assurance.

DEPT® employs automated testing to ensure responses align with expectations. Briski also stressed the importance of setting stringent parameters to guide AI conversations, ensuring alignment with the company’s ethos and desired consumer interactions.

“It’s important to really keep focused on the exact conversation you want to have with your consumer or your customer and put really strict guardrails around how you would like the model to answer those questions,” explains Briski.

In December, DEPT® is sponsoring AI & Big Data Expo Global and will be in attendance to share its unique insights. Briski is a speaker at the event and will be providing a deep dive into business intelligence (BI), illuminating strategies to enhance responsiveness through large language models.

“I’ll be diving into how we can transform BI to be much more responsive to the business, especially with the help of large language models,” says Briski.

As DEPT® continues to redefine digital paradigms, we look forward to observing how the company’s innovations deliver a new era in AI-powered experiences.

DEPT® is a key sponsor of this year’s AI & Big Data Expo Global on 30 Nov – 1 Dec 2023. Swing by DEPT®’s booth to hear more about AI and LLMs from the company’s experts and watch Briski’s day one presentation.

The post Bob Briski, DEPT®:  A dive into the future of AI-powered experiences appeared first on AI News.

How to create a digital marketing strategy with AI

AI has opened up a vast array of new possibilities for digital marketers, enabling them to gain competitive advantages and engage with customers in innovative ways. Among such tools are artificial intelligence marketing platforms, which in essence, are software that leverage AI technology to perform tasks and help marketers make insightful business decisions.

From predicting customer behaviour to automating tasks, AI marketing tools can help marketers save time, increase efficiency, optimise efforts, and boost ROI by identifying patterns from historical marketing data. 

In this article, we’ll delve into the world of AI marketing strategy, exploring how to create a powerful digital marketing plan by harnessing AI-driven tools and technology.

The power of AI in marketing

Using AI for marketing has rapidly become a game-changer in the world of digital marketing. Its ability to process vast amounts of data, analyse patterns, and make real-time decisions has made it an indispensable tool for marketing teams. Here’s how you can harness the potential of AI to supercharge your marketing strategy:

1. Understand your target audience

One of the primary advantages of AI in marketing is its ability to analyse customer data comprehensively. With AI-driven tools, you can gain valuable insights into consumer behaviour, preferences, and buying patterns. This information is instrumental in creating highly targeted marketing campaigns that resonate with your audience.

2. Personalise content

AI technology enables you to personalise your content like never before. By analysing user data, AI can tailor marketing messages and product recommendations to individual customers. This personal touch not only enhances the customer experience but also boosts conversion rates.

3. Content generation

Creating engaging content is a fundamental aspect of any marketing strategy. AI can assist marketing teams by generating content ideas, optimising headlines, and even drafting articles. This streamlines the content creation process, allowing marketers to focus on strategy and creativity.

4. Real-time decision-making

In the fast-paced world of digital marketing, timing is crucial. AI can analyse data in real-time, helping you make informed decisions on when and where to run your marketing campaigns. This ensures that your efforts always align with the latest trends and consumer behaviour.

Now that we’ve explored the incredible potential of AI in marketing, let’s dive into the tools of AI for marketing strategy.

Seven best AI tools that help you to create a marketing strategy

Adzooma

Adzooma stands as a formidable AI marketing ally, offering an array of invaluable benefits for optimising your AI marketing strategy. With its AI-powered recommendations, it seamlessly identifies avenues for enhancing campaign performance. The platform’s automation capabilities extend to managing bid adjustments, fine-tuning keyword targeting, and conducting ad copy tests, significantly streamlining your campaign management tasks. By harnessing Adzooma, not only do you achieve time efficiency, but you also elevate the effectiveness of your AI marketing endeavours, yielding superior results.

Jasper

Jasper AI, formerly recognized as Conversion.ai, represents an advanced AI marketing solution tailored for crafting top-notch content across various channels, including social media, email, landing pages, and articles. It harnesses the capabilities of the GPT-3 model developed by OpenAI to transform text into engaging copy. Marketers can effortlessly generate compelling content by supplying input such as brand or product names. With this potent AI marketing tool, businesses spanning diverse sectors gain the capacity to craft more convincing, alluring content for their blog posts, landing pages, social media, advertisements, marketing emails, and beyond.

HubSpot

HubSpot’s AI tools empower marketers to unlock new levels of creativity and precision in their campaigns, ultimately driving enhanced engagement and results. Transform your marketing approach with HubSpot’s cutting-edge AI capabilities. Marketers now have access to powerful tools that facilitate content creation, campaign personalization, and data analysis, enabling the development of precise and influential marketing strategies. Additionally, it assists in crafting social media posts and generating images based on user prompts, ensuring that your marketing efforts are streamlined, efficient, and highly impactful. 

Murf AI

Murf stands as an AI voice synthesiser, ranking among the foremost AI marketing solutions. It empowers marketers to generate lifelike speech through natural language processing, eliminating the necessity for specialised hardware. This AI marketing solution streamlines marketers’ processes, facilitating the swift creation of audio and video content for various applications, such as social media, brand advertising, and marketing initiatives, all within a matter of minutes.

Adobe Sensei

Adobe’s commitment to innovation ensures that brands can leverage AI-driven marketing solutions to elevate their customer experiences, paving the way for enhanced engagement and loyalty. As a pioneering force in artificial intelligence, we recognise that generative AI holds the power to revolutionise all facets of marketing, spanning the realms of strategy, implementation, and analysis. With the introduction of Adobe Sensei GenAI, their latest AI technology, customer experience teams can harness the innate capabilities of generative AI. This empowers them to amplify their expertise and deliver customer journeys that are not only more precise but also deeply personalised. 

ClickUp

The introduction of ClickUp AI marks a transformative moment for marketing and sales units, offering enhanced efficiency, seamless collaboration, and expedited workflows. This innovative feature within ClickUp introduces AI capabilities that are poised to revolutionise the way teams operate. From streamlining processes and fostering creativity to facilitating project management and igniting innovative thinking, ClickUp AI is the catalyst for optimising productivity and content quality, all while ensuring consistency and scalability.

SurferSEO

SurferSEO is an AI-powered SEO tool that helps you optimise your content for search engines. It analyses the top-ranking pages for your target keywords and provides you with insights into their content structure, word count, and other factors. You can then use this information to create content that is more likely to rank well.

Final thoughts

As AI technology continues to evolve, its role in marketing will only become more prominent. By harnessing the power of AI-driven tools and technology, you can create a dynamic and data-driven marketing strategy that not only reaches your target audience but also engages them on a personalised level. Embrace the future of marketing with AI and elevate your brand to new heights.

(Photo by Johannes Plenio on Unsplash)

The post Supercharge your AI marketing strategy: Top 7 tools to automate and optimise appeared first on AI News.

Dimon expressed his belief that AI has the potential to revolutionise every aspect of JPMorgan’s operations, from trading and hedging to research and error detection. He described AI as a “living, breathing thing,” capable of transforming traditional processes and augmenting human capabilities.

Dimon’s enthusiasm for AI is grounded in its current applications within JPMorgan. He revealed that AI is already extensively used in equity hedging, idea generation, and large language models.

Despite the ongoing debate about the impact of AI on employment, Dimon remains pragmatic. He acknowledged that AI will replace certain jobs, but he emphasised that technology has historically led to job displacement and this evolution is a natural part of progress.

One of Dimon’s main concerns about AI technology revolves around its potential misuse by malicious actors, especially in cyberspace. He stressed the importance of establishing legal safeguards to prevent the misuse of AI.

Despite these concerns, Dimon remains optimistic about the positive impact of AI on the workforce and society. He highlighted the benefits of other technological breakthroughs, many of which can be further enhanced using AI.

“Your children will live to 100 and not have cancer because of technology, and they’ll probably be working three days a week. So technology’s done unbelievable things for mankind,” said Dimon.

Dimon outlined JPMorgan’s proactive approach to potential job displacement caused by AI implementation. He expressed the firm’s commitment to supporting employees who might be affected, stating that they plan to redeploy displaced workers in local branches or different functions within the company.

Dimon’s forward-thinking approach highlights the transformative power of AI in shaping the future of finance and other industries. However, it also reiterates the need for consideration of how to minimise negative impacts such as job displacement.

(Image Credit: Stuart Isett/Fortune Global Forum under CC BY-NC-ND 2.0 DEED license)

See also: Cyber Security & Cloud Expo: The alarming potential of AI-powered cybercrime

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.

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

The post JPMorgan CEO: AI will be used for ‘every single process’ appeared first on AI News.

The internet, once seen as a source of knowledge for all, has now become a complex maze. Although traditional search engines are powerful, they often inundate users with a flood of results, making it difficult to find what they are searching for. The emergence of new technologies like ChatGPT from OpenAI has been impressive, along with other language models such as Bard. However, these models also come with certain drawbacks for business users, such as the risk of generating inaccurate information, a lack of proper citation, potential copyright infringements, and a scarcity of reliable information in the business domain. The challenge lies not only in finding information but in finding the right information. In order to make Generative AI effective in the business world, we must address these concerns, which is the focal point of RAG.

The digital challenge: A sea of information

At the corner of platforms like Microsoft Copilot and Lucy is the transformative approach of the Retrieval-Augmented Generation (RAG) model.

Understanding RAG

What precisely is RAG, and how does it work? In simple terms, RAG is a two-step process:

1. Retrieval: Prior to providing an answer, the system delves into an extensive database, meticulously retrieving pertinent documents or passages. This isn’t a rudimentary matching of keywords; it’s a cutting-edge process that comprehends the intricate context and nuances of the query. RAG systems rely on the data owned or licensed by companies, and ensure that Enterprise Levels of access control are impeccably managed and preserved.

2. Generation: Once the pertinent information is retrieved, it serves as the foundation for generating a coherent and contextually accurate response. This isn’t just about regurgitating data; it’s about crafting a meaningful and informative answer.

By integrating these two critical processes, RAG ensures that the responses delivered are not only precise but also well-informed. It’s akin to having a dedicated team of researchers at your disposal, ready to delve into a vast library, select the most appropriate sources, and present you with a concise and informative summary.

Why RAG matters

Leading technology platforms that have embraced RAG – such as Microsoft Copilot for content creation or federated search platforms like Lucy – represent a significant breakthrough for several reasons:

1. Efficiency: Traditional models often demand substantial computational resources, particularly when dealing with extensive datasets. RAG, with its process segmentation, ensures efficiency, even when handling complex queries.

2. Accuracy: By first retrieving relevant data and then generating a response based on that data, RAG guarantees that the answers provided are firmly rooted in credible sources, enhancing accuracy and reliability.

3. Adaptability: RAG’s adaptability shines through as new information is continually added to the database. This ensures that the answers generated by platforms remain up-to-date and relevant.

RAG platforms in action

Picture yourself as a financial analyst seeking insights into market trends. Traditional research methods would require hours, if not days, to comb through reports, articles, and data sets. Lucy, however, simplifies the process – you merely pose your question. Behind the scenes, the RAG model springs into action, retrieving relevant financial documents and promptly generating a comprehensive response, all within seconds.

Similarly, envision a student conducting research on a historical event. Instead of becoming lost in a sea of search results, Lucy, powered by RAG, provides a concise, well-informed response, streamlining the research process and enhancing efficiency.

Take this one step further, Lucy feeds these answers across a complex data ecosystem to Microsoft Copilot and new presentations or documentation is created leveraging all of the institutional knowledge an organisation has created or purchased..

The road ahead

The potential applications of RAG are expansive, spanning academia, industry, and everyday inquiries. Beyond its immediate utility, RAG signifies a broader shift in our interaction with information. In an age of information overload, tools like Microsoft Copilot and Lucy, powered by RAG, are not merely conveniences; they are necessities.

Furthermore, as technology continues to evolve, we can anticipate even more sophisticated iterations of the RAG model, promising heightened accuracy, efficiency, and user experience. Working with platforms that have embraced RAG from the onset (or before even a term) will keep your organisation ahead of the curve.

Conclusion

In the digital era, we face both challenges and opportunities. While the sheer volume of information can be overwhelming, technologies like Microsoft Copilot or Lucy, underpinned by the potency of Retrieval-Augmented Generation, offer a promising path forward. This is a testament to technology’s potential not only to manage but also to meaningfully engage with the vast reservoirs of knowledge at our disposal. These aren’t just platforms; they are a glimpse into the future of information retrieval.

Photo by Markus Winkler on Unsplash

The post How information retrieval is being revolutionised with RAG technology appeared first on AI News.

Anthropic was founded by siblings Dario and Daniela Amodei, who were previously associated with OpenAI. This latest investment signifies Amazon’s strategic move to bolster its presence in the ever-intensifying AI arena.

“We are excited to use AWS’s Trainium chips to develop future foundation models,” said Dario Amodei, co-founder and CEO of Anthropic. “Since announcing our support of Amazon Bedrock in April, Claude has seen significant organic adoption from AWS customers.

“By significantly expanding our partnership, we can unlock new possibilities for organisations of all sizes, as they deploy Anthropic’s safe, state-of-the-art AI systems together with AWS’s leading cloud technology.”

While Amazon’s investment in Anthropic may seem overshadowed by Microsoft’s reported $13 billion commitment to OpenAI, it is a clear indication of Amazon’s ambition in the rapidly-evolving AI landscape. The collaboration between Amazon and Anthropic holds the promise of reshaping the AI sector with innovative developments.

“We have tremendous respect for Anthropic’s team and foundation models, and believe we can help improve many customer experiences, short and long-term, through our deeper collaboration,” said Andy Jassy, CEO of Amazon.

“Customers are quite excited about Amazon Bedrock, AWS’ new managed service that enables companies to use various foundation models to build generative AI applications on top of, as well as AWS Trainium, AWS’ AI training chip, and our collaboration with Anthropic should help customers get even more value from these two capabilities.”

Anthropic’s flagship product, the Claude AI model, distinguishes itself by claiming a higher level of safety compared to its competitors.

Claude and its advanced iteration, Claude 2, are large language model-based chatbots similar in functionality to OpenAI’s ChatGPT and Google’s Bard. They excel in tasks like text translation, code generation, and answering a variety of questions.

What sets Claude apart is its ability to autonomously revise responses, eliminating the need for human moderation. This unique feature positions Claude as a safer and more dependable AI tool, especially in contexts where precise, unbiased information is crucial.

Claude’s capacity to handle larger prompts also makes it particularly suitable for tasks involving extensive business or legal documents, offering a valuable edge in industries reliant on meticulous data analysis.

As part of this strategic investment, Amazon will acquire a minority ownership stake in Anthropic. Amazon is set to integrate Anthropic’s cutting-edge technology into a range of its products, including the Amazon Bedrock service, designed for building AI applications. 

In return, Anthropic will leverage Amazon’s custom-designed chips for the development, training, and deployment of its future AI foundation models. The partnership also solidifies Anthropic’s commitment to Amazon Web Services (AWS) as its primary cloud provider.

In the initial phase, Amazon has committed $1.25 billion to Anthropic, with an option to increase its investment by an additional $2.75 billion. If the full $4 billion investment materialises, it will become the largest publicly-known investment linked to AWS.

Anthropic’s partnership with Amazon comes alongside its existing collaboration with Google, where Google holds approximately a 10 percent stake following a $300 million investment earlier this year. Anthropic has affirmed its intent to maintain this relationship with Google and continue offering its technology through Google Cloud, showcasing its commitment to broadening its reach across the industry.

In a rapidly-advancing landscape, Amazon’s strategic investment in Anthropic underscores its determination to remain at the forefront of AI innovation and sets the stage for exciting future developments.

(Image Credit: Anthropic)

See also: OpenAI reveals DALL-E 3 text-to-image model

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.

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

The post Amazon invests $4B in Anthropic to boost AI capabilities appeared first on AI News.

DALL-E 3 promises significant enhancements over its predecessors and introduces seamless integration with ChatGPT.

One of the standout features of DALL-E 3 is its ability to better understand and interpret user intentions when confronted with detailed and lengthy prompts:

"A middle-aged woman of Asian descent, her dark hair streaked with silver, appears fractured and splintered, intricately embedded within a sea of broken porcelain. The porcelain glistens with splatter paint patterns in a harmonious blend of glossy and matte blues, greens,… pic.twitter.com/QryQz0BtxB

— OpenAI (@OpenAI) September 20, 2023

Even if a user struggles to articulate their vision precisely, ChatGPT can step in to assist in crafting comprehensive prompts.

DALL-E 3 has been engineered to excel in creating elements that its predecessors and other AI generators have historically struggled with, such as rendering intricate depictions of hands and incorporating text into images:

"An illustration of a human heart made of translucent glass, standing on a pedestal amidst a stormy sea. Rays of sunlight pierce the clouds, illuminating the heart, revealing a tiny universe within. The quote 'Find the universe within you' is etched in bold letters across the… pic.twitter.com/S3cKMkG67T

— OpenAI (@OpenAI) September 20, 2023

OpenAI has also implemented robust security measures, ensuring the AI system refrains from generating explicit or offensive content by identifying and ignoring certain keywords in prompts.

Beyond technical advancements, OpenAI has taken steps to mitigate potential legal issues. 

While the current DALL-E version can mimic the styles of living artists, the forthcoming DALL-E 3 has been designed to decline requests to replicate their copyrighted works. Artists will also have the option to submit their original creations through a dedicated form on the OpenAI website, allowing them to request removal if necessary.

OpenAI’s rollout plan for DALL-E 3 involves an initial release to ChatGPT ‘Plus’ and ‘Enterprise’ customers next month. The enhanced image generator will then become available to OpenAI’s research labs and API customers in the upcoming fall season.

As OpenAI continues to push the boundaries of AI technology, DALL-E 3 represents a major step forward in text-to-image generation.

(Image Credit: OpenAI)

See also: Stability AI unveils ‘Stable Audio’ model for controllable audio generation

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.

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

The post OpenAI reveals DALL-E 3 text-to-image model appeared first on AI News.

This breakthrough promises to be another leap forward for generative AI and combines text metadata, audio duration, and start time conditioning to offer unprecedented control over the content and length of generated audio—even enabling the creation of complete songs.

Audio diffusion models traditionally faced a significant limitation in generating audio of fixed durations, often leading to abrupt and incomplete musical phrases. This was primarily due to the models being trained on random audio chunks cropped from longer files and then forced into predetermined lengths.

Stable Audio effectively tackles this historic challenge, enabling the generation of audio with specified lengths, up to the training window size.

One of the standout features of Stable Audio is its use of a heavily downsampled latent representation of audio, resulting in vastly accelerated inference times compared to raw audio. Through cutting-edge diffusion sampling techniques, the flagship Stable Audio model can generate 95 seconds of stereo audio at a 44.1 kHz sample rate in under a second utilising the power of an NVIDIA A100 GPU.

A sound foundation

The core architecture of Stable Audio comprises a variational autoencoder (VAE), a text encoder, and a U-Net-based conditioned diffusion model.

The VAE plays a pivotal role by compressing stereo audio into a noise-resistant, lossy latent encoding that significantly expedites both generation and training processes. This approach, based on the Descript Audio Codec encoder and decoder architectures, facilitates encoding and decoding of arbitrary-length audio while ensuring high-fidelity output.

To harness the influence of text prompts, Stability AI utilises a text encoder derived from a CLAP model specially trained on their dataset. This enables the model to imbue text features with information about the relationships between words and sounds. These text features, extracted from the penultimate layer of the CLAP text encoder, are integrated into the diffusion U-Net through cross-attention layers.

During training, the model learns to incorporate two key properties from audio chunks: the starting second (“seconds_start”) and the total duration of the original audio file (“seconds_total”). These properties are transformed into discrete learned embeddings per second, which are then concatenated with the text prompt tokens. This unique conditioning allows users to specify the desired length of the generated audio during inference.

The diffusion model at the heart of Stable Audio boasts a staggering 907 million parameters and leverages a sophisticated blend of residual layers, self-attention layers, and cross-attention layers to denoise the input while considering text and timing embeddings. To enhance memory efficiency and scalability for longer sequence lengths, the model incorporates memory-efficient implementations of attention.

To train the flagship Stable Audio model, Stability AI curated an extensive dataset comprising over 800,000 audio files encompassing music, sound effects, and single-instrument stems. This rich dataset, furnished in partnership with AudioSparx – a prominent stock music provider – amounts to a staggering 19,500 hours of audio.

Stable Audio represents the vanguard of audio generation research, emerging from Stability AI’s generative audio research lab, Harmonai. The team remains dedicated to advancing model architectures, refining datasets, and enhancing training procedures. Their pursuit encompasses elevating output quality, fine-tuning controllability, optimising inference speed, and expanding the range of achievable output lengths.

Stability AI has hinted at forthcoming releases from Harmonai, teasing the possibility of open-source models based on Stable Audio and accessible training code.

This latest groundbreaking announcement follows a string of noteworthy stories about Stability. Earlier this week, Stability joined seven other prominent AI companies that signed the White House’s voluntary AI safety pledge as part of its second round.

You can try Stable Audio for yourself here.

(Photo by Eric Nopanen 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 Digital Transformation Week.

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

The post Stability AI unveils ‘Stable Audio’ model for controllable audio generation appeared first on AI News.

The report, titled ‘The State of AI in Software Development,’ presents insights from over 1,000 global senior technology executives, developers, and security and operations professionals.

The report reveals a complex relationship between enthusiasm for AI adoption and concerns about data privacy, intellectual property, and security.

“Enterprises are seeking out platforms that allow them to harness the power of AI while addressing potential privacy and security risks,” said Alexander Johnston, Research Analyst in the Data, AI & Analytics Channel at 451 Research, a part of S&P Global Market Intelligence.

While 83 percent of the survey’s respondents view AI implementation as essential to stay competitive, a significant 79 percent expressed worries about AI tools accessing sensitive information and intellectual property.

Impact on developer productivity

AI is perceived as a boon for developer productivity, with 51 percent of all respondents citing it as a key benefit of AI implementation. However, security professionals are apprehensive that AI-generated code might lead to an increase in security vulnerabilities, potentially creating more work for them.

Only seven percent of developers’ time is currently spent identifying and mitigating security vulnerabilities, compared to 11 percent allocated to testing code. This raises questions about the widening gap between developers and security professionals in the AI era.

Privacy and intellectual property concerns

The survey underscores the paramount importance of data privacy and intellectual property protection when selecting AI tools. 95 percent of senior technology executives prioritise these aspects when choosing AI solutions.

Moreover, 32 percent of respondents admitted to being “very” or “extremely” concerned about introducing AI into the software development lifecycle. Within this group, 39 percent cited worries about AI-generated code introducing security vulnerabilities, and 48 percent expressed concerns that AI-generated code may not receive the same copyright protection as code produced by humans.

AI skills gap

Despite optimism about AI’s potential, the report identifies a disconnect between organisations’ provision of AI training resources and practitioners’ satisfaction with them. 

While 75 percent of respondents stated that their organisations offer training and resources for using AI, an equivalent proportion expressed the need to seek resources independently—suggesting that the available training may be insufficient.

A striking 81 percent of respondents said they require more training to effectively utilise AI in their daily work. Furthermore, 65 percent of those planning to use AI for software development indicated that their organsations plan to hire new talent to manage AI implementation.

David DeSanto, Chief Product Officer at GitLab, said:

“According to the GitLab Global DevSecOps Report, only 25 percent of developers’ time is spent on code generation, but the data shows AI can boost productivity and collaboration in nearly 60 percent of developers’ day-to-day work.

To realise AI’s full potential, it needs to be embedded across the software development lifecycle, allowing everyone involved in delivering secure software – not just developers – to benefit from the efficiency boost.” 

While AI holds immense promise for the software development industry, GitLab’s report makes it clear that addressing cybersecurity and privacy concerns, bridging the skills gap, and fostering collaboration between developers and security professionals are pivotal to successful AI adoption.

(Photo by Luca Bravo on Unsplash)

See also: UK government outlines AI Safety Summit plans

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