Google And Universal Music Reportedly Discuss AI-Generated Music via @sejournal, @kristileilani

Google and Universal Music (and other music companies) may be in negotiations to license artists’ voices and melodies for songs generated by artificial intelligence (AI).

This development, initially reported by the Financial Times, emerges as the music industry faces new challenges and opportunities in monetizing AI-generated deepfake songs.

Negotiating AI-Generated Music

Technology that can convincingly replicate the voices of established artists has been a pressing concern for music corporations.

In response, Google and Universal Music could be in early talks to allow fans to legally create tracks using AI-generated voices while paying the rightful copyright owners. Artists would have the option to participate.

With deepfake songs already mimicking voices like Frank Sinatra or Johnny Cash, the issue is no longer a distant threat but a current reality. The goal now would be to bring it into a monetizable framework.

Artists such as Drake and Taylor Swift have been “featured” in AI-generated songs that have gone viral.

A Fine Line Between Innovation And Infringement

As AI gains traction in the music industry, some musicians have voiced concern that their work may be diluted by fake versions of their songs and voices.

Others, such as electronic artist Grimes, have embraced the technology.

For Google, creating a music product powered by AI could help the company compete with rivals – like Meta – who are also developing AI audio products.

However, the issue of licensing and copyright in the age of AI-generated music is much more complex.

It will be a delicate balance for corporations between respecting artists’ rights, pushing the boundaries of AI innovation, and making a profit.

MusicLM: High-Quality AI-Generated Music From Text

In related news, Google introduced MusicLM in January. Users could sign up to test MusicLM in the AI Test Kitchen in May.

By simply typing prompts like “soulful jazz for a dinner party,” users of the experimental tool can explore two versions of a song and vote on their preference, aiding in the refinement of the model.

The model’s capabilities go further in that it can be conditioned on both text and melody, transforming whistled and hummed tunes according to the style described in a text caption.

While MusicLM is an experimental tool to generate synthetic music for inspiration, it has certain constraints.

Specific queries mentioning artists or including vocals will not be produced, and users are encouraged to provide feedback if any issues arise with the generated audio.

This may be where a partnership with Universal Music comes into play. Warner Music, another significant label, may also be in talks with Google for similar reasons.

Meta’s AudioCraft

At the beginning of August, Meta announced AudioCraft as a new tool for musicians and sound designers, potentially shaping how we produce and consume audio and music.

AudioCraft consists of three primary models: MusicGen, AudioGen, and EnCodec. MusicGen, backed by Meta-licensed music, facilitates music creation from text prompts. AudioGen, trained in public sound effects, brings the text to life through sounds like a dog barking or cars honking.

The company is open-sourcing these models, granting access to researchers and practitioners to train their models for the first time. This move seeks to drive the field of AI-generated audio and music forward.

The excitement around generative AI has surged, but the audio has lagged. High-fidelity audio requires modeling complex signals and patterns, making music generation incredibly challenging.

Meta hopes the AudioCraft family simplifies this process. Its open structure could allow individuals to build better sound generators, compression algorithms, or music generators.

The Future Of AI-Generated Music

Recent negotiations and developments from Google and Meta mark significant leaps in AI, opening doors for music professionals, enthusiasts, and content creators to explore new creative horizons.

It seems clear that big tech companies want to be the first to launch user-friendly platforms that translate ideas into musical reality.

The development also hints at the future direction of AI in music, offering insights into potential opportunities and risks.


Featured image: Sundry Photography/Shutterstock

OpenAI Launches GPTBot With Details On How To Restrict Access via @sejournal, @kristileilani

OpenAI has launched GPTBot, a new web crawler to improve future artificial intelligence models like GPT-4 and the future GPT-5.

How GPTBot Works

Recognizable by the following user agent token and the entire user-agent string, this system scours the web for data that can enhance AI technology’s accuracy, capabilities, and safety.

User agent token: GPTBot
Full user-agent string: Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)

Reportedly, it should strictly filter out any paywall-restricted sources, sources that violate OpenAI’s policies, or sources that gather personally identifiable information.

The utilization of GPTBot can potentially provide a significant boost to AI models.

By allowing it to access your site, you contribute to this data pool, thereby improving the overall AI ecosystem.

However, it’s not a one-size-fits-all scenario. OpenAI has given web admins the power to choose whether or not to grant GPTBot access to their websites.

Restricting GPTBot Access

If website owners wish to restrict GPTBot from their site, they can modify their robots.txt file.

By including the following, they can prevent GPTBot from accessing the entirety of their website.

User-agent: GPTBot
Disallow: /

In contrast, those who wish to grant partial access can customize the directories that GPTBot can access. To do this, add the following to the robots.txt file.

User-agent: GPTBot
Allow: /directory-1/
Disallow: /directory-2/

Regarding the technical operations of GPTBot, any calls made to websites originate from IP address ranges documented on OpenAI’s website. This detail provides added transparency and clarity to web admins about the traffic source on their sites.

Allowing or disallowing the GPTBot web crawler could significantly affect your site’s data privacy, security, and contribution to AI advancement.

Legal And Ethical Concerns

OpenAI’s latest news has sparked a debate on Hacker News around the ethics and legality of using scraped web data to train proprietary AI systems.

GPTBot identifies itself so web admins can block it via robots.txt, but some argue there’s no benefit to allowing it, unlike search engine crawlers that drive traffic. A significant concern is copyrighted content being used without attribution. ChatGPT does not currently cite sources.

There are also questions about how GPTBot handles licensed images, videos, music, and other media found on websites. If that media ends in model training, it could constitute copyright infringement. Some experts think crawler-generated data could degrade models if AI-written content gets fed back into training.

Conversely, some believe OpenAI has the right to use public web data freely, likening it to a person learning from online content. However, others argue that OpenAI should share profits if it monetizes web data for commercial gain.

Overall, GPTBot has opened complex debates around ownership, fair use, and the incentives of web content creators. While following robots.txt is a good step, transparency is still lacking. The tech community wonders how their data will be used as AI products advance rapidly.


Featured image: Vitor Miranda/Shutterstock

OpenAI Files Trademark Application for GPT-5 via @sejournal, @kristileilani

OpenAI OpCo, LLC has filed an application for the trademark “GPT-5” with the United States Patent and Trademark Office (USPTO).

OpenAI Files Trademark Application for GPT-5Screenshot from USPTO, August 2023

The application, filed on July 18, 2023, is currently in process.

Additional Details About The GPT-5 Trademark Application

Initially reported by Windows Latest and shared in multiple tweets, the trademark registration is intended to cover a broad range of categories. Primarily, they cover downloadable computer programs and software related to language models, artificial production of human speech and text, natural language processing, generation, understanding, and analysis.

The application also includes software for machine-learning-based language and speech processing, translation of text or speech from one language to another, sharing datasets for machine learning, predictive analytics, and building language models.

Additional features include converting audio data files into text, voice and speech recognition, creating and generating text, and developing, running, and analyzing algorithms that can learn to analyze, classify, and take actions in response to data exposure.

Furthermore, the application extends to software for developing and implementing artificial neural networks. OpenAI also intends to provide Software as a Service (SaaS) for these functions.

The application is currently in the “new application processing” stage, meaning the office has accepted it and is awaiting assignment to an examining attorney.

For comparison, OpenAI OpCo, LLC filed a similar application for GPT-4 on March 13, 2023.

The USPTO website notes that it is processing applications submitted between September 29, 2022 – October 13, 2022.

“We Have A Lot Of Work To Do…”

The trademarking of GPT-5 by OpenAI could indicate many things.

At a recent event, Sam Altman, OpenAI CEO, discussed the development of GPT-5.

“We have a lot of work to do before GPT 5. It takes a lot of time for it. We are not certainly close to it. There needs to be more safety audits. I wish I could tell you about the timeline of the next GPT”

While it may not mean that a new, more powerful LLM will be available soon, the application filing does signify a continued advancement of AI technology, particularly in natural language processing and machine learning.


Featured image: Vladimka production/Shutterstock

OpenAI, Google, Microsoft, and Anthropic Form AI Safety Forum via @sejournal, @kristileilani

In a significant move towards ensuring the safe and responsible development of frontier AI models, four major tech companies – OpenAI, Google, Microsoft, and Anthropic – announced the formation of the Frontier Model Forum.

This new industry body aims to draw on its member companies’ technical and operational expertise to benefit the AI ecosystem.

Frontier Model Forum’s Key Focus

The Frontier Model Forum will focus on three key areas over the coming year.

Firstly, it will identify best practices to promote knowledge sharing among industry, governments, civil society, and academia, focusing on safety standards and procedures to mitigate potential risks.

Secondly, it will advance AI safety research by identifying the most important open research questions on AI safety.

The Forum will coordinate research efforts in adversarial robustness, mechanistic interpretability, scalable oversight, independent research access, emergent behaviors, and anomaly detection.

Lastly, it will facilitate information sharing among companies and governments by establishing trusted, secure mechanisms for sharing information regarding AI safety and risks.

The Forum defines frontier models as large-scale machine-learning models that exceed the capabilities currently in the most advanced existing models and can perform various tasks.

Forum Membership Requirements

Membership is open to organizations that develop and deploy frontier models, demonstrate a solid commitment to frontier model safety, and are willing to contribute to advancing the Forum’s efforts.

In addition, the Forum will establish an Advisory Board to guide its strategy and priorities.

The founding companies will also establish vital institutional arrangements, including a charter, governance, and funding, with a working group and executive board to lead these efforts.

The Forum plans to consult with civil society and governments in the coming weeks on the design of the Forum and on meaningful ways to collaborate.

The Frontier Model Forum will also seek to build on the valuable work of existing industry, civil society, and research efforts across each workstream.

Initiatives such as the Partnership on AI and MLCommons continue to contribute to the AI community significantly. The Forum will explore ways to collaborate with and support these and other valuable multistakeholder efforts.

The leaders of the founding companies expressed their excitement and commitment to the initiative.

“We’re excited to work together with other leading companies, sharing technical expertise to promote responsible AI innovation. Engagement by companies, governments, and civil society will be essential to fulfill the promise of AI to benefit everyone.”

Kent Walker, President, Global Affairs, Google & Alphabet

“Companies creating AI technology have a responsibility to ensure that it is safe, secure, and remains under human control. This initiative is a vital step to bring the tech sector together in advancing AI responsibly and tackling the challenges so that it benefits all of humanity.”

Brad Smith, Vice Chair & President, Microsoft

“Advanced AI technologies have the potential to profoundly benefit society, and the ability to achieve this potential requires oversight and governance. It is vital that AI companies – especially those working on the most powerful models – align on common ground and advance thoughtful and adaptable safety practices to ensure powerful AI tools have the broadest benefit possible. This is urgent work and this forum is well– positioned to act quickly to advance the state of AI safety.”

Anna Makanju, Vice President of Global Affairs, OpenAI

“Anthropic believes that AI has the potential to fundamentally change how the world works. We are excited to collaborate with industry, civil society, government, and academia to promote safe and responsible development of the technology. The Frontier Model Forum will play a vital role in coordinating best practices and sharing research on frontier AI safety.”

Dario Amodei, CEO, Anthropic

Red Teaming For Safety

Anthropic, in particular, highlighted the importance of cybersecurity in developing frontier AI models.

The makers of Claude 2 have recently unveiled its strategy for “red teaming,” an adversarial testing technique aimed at bolstering AI systems’ safety and security.

This intensive, expertise-driven method evaluates risk baselines and establishes consistent practices across numerous subject domains.

As part of their initiative, Anthropic conducted a classified study into biological risks, concluding that unmitigated models could pose imminent threats to national security.

Yet, the company also identified substantial mitigating measures that could minimize these potential hazards.

The frontier threats red teaming process involves working with domain experts to define threat models, developing automated evaluations based on expert insights, and ensuring the repeatability and scalability of these evaluations.

In their biosecurity-focused study involving more than 150 hours of red teaming, Anthropic discovered that advanced AI models can generate intricate, accurate, and actionable knowledge at an expert level.

As models increase in size and gain access to tools, their proficiency, particularly in biology, heightens, potentially actualizing these risks within two to three years.

Anthropic’s research led to the discovery of mitigations that reduce harmful outputs during the training process and make it challenging for malevolent actors to obtain detailed, linked, expert-level information for destructive purposes.

Currently, these mitigations are integrated into Anthropic’s public-facing frontier model, with further experiments in the pipeline.

AI Companies Commit To Managing AI Risks

Last week, the White House brokered voluntary commitments from seven principal AI companies—Amazon, OpenAI, Google, Microsoft, Inflection, Meta, and Anthropic.

The seven AI companies, signifying the future of technology, were entrusted with the responsibility of ensuring the safety of their products.

The Biden-Harris Administration stressed the need to uphold the highest standards to ensure that innovative strides are not taken at the expense of American citizens’ rights and safety.

The three guiding principles that the participating companies are committed to are safety, security, and trust.

Before shipping a product, the companies pledged to complete internal and external security testing of AI systems, managed partly by independent experts. The aim would be to counter risks such as biosecurity, cybersecurity, and broader societal effects.

Security was at the forefront of these commitments, promising to bolster cybersecurity and establish insider threat safeguards to protect proprietary and unreleased model weights, the core component of an AI system.

To instill public trust, companies also committed to the creation of robust mechanisms to inform users when content is AI-generated.

They also pledged to issue public reports on AI systems’ capabilities, limitations, and usage scope. These reports could shed light on security and societal risks, including the effects on fairness and bias.

Further, these companies are committed to advancing AI systems to address some of the world’s most significant challenges, including cancer prevention and climate change mitigation.

As part of the agenda, the administration plans to work with international allies and partners to establish a robust framework governing the development and use of AI.

Public Voting On AI Safety

In June, OpenAI launched an initiative with the Citizens Foundation and The Governance Lab to ascertain public sentiment on AI safety.

A website was created to foster discussion about the potential risks associated with LLMs.

Public members could vote on AI safety priorities via a tool known as AllOurIdeas. It was designed to help understand the public’s prioritization of various considerations associated with AI risks.

The tool employs a method called “Pairwise Voting,” which prompts users to juxtapose two potential AI risk priorities and select the one they deem more crucial.

The objective is to glean as much information as possible about public concerns, thus directing resources more effectively toward addressing the issues that people find most pressing.

The votes helped to gauge public opinion about the responsible development of AI technology.

In the coming weeks, a virtual roundtable discussion will be organized to evaluate the results of this public consultation.

A GPT-4 analysis of the votes determined that the top three ideas for AI were as follows.

  1. Models need to be as intelligent as possible and recognize the biases in their training data.
  2. Everyone, regardless of their race, religion, political leaning, gender, or income, should have access to impartial AI technology.
  3. The cycle of AI aiding in the progress of knowledge, which serves as the foundation for AI, should not impede progress.

Conversely, there were three unpopular ideas:

  1. A balanced approach would involve government bodies providing guidance, which AI companies can use to create their advice.
  2. Advanced weaponry kill/live decisions are not made using AI.
  3. Using this for political or religious purposes is not recommended as it may create a new campaigning approach.

The Future Of AI Safety

As AI plays an increasingly prominent role in search and digital marketing, these developments hold substantial significance for those in marketing and tech.

These commitments and initiatives made by leading AI companies could shape AI regulations and policleading lead to a future of responsible AI development.


Featured image: Derek W/Shutterstock

Meta And Microsoft Release Llama 2 Free For Commercial Use And Research via @sejournal, @kristileilani

Meta and Microsoft announced an expanded artificial intelligence partnership with the release of their new large language model (LLM), Llama 2, free for research and commercial use.

This marks the latest trend toward an open LLM development and training approach.

Meta Releases Llama 2

Meta’s announced Llama 2, now accessible through Microsoft Azure, Amazon Web Services, Hugging Face, and other providers.

The post highlighted how increased access to foundational AI technologies can benefit businesses globally. Meta aimed to let developers and researchers stress-test LLMs to identify and fix problems faster.

Meta conveyed the belief that opening up access is safer than limiting availability. It hoped the AI community would collaborate on improving tools and addressing vulnerabilities.

Meta also announced an expanded partnership with Microsoft, making Microsoft the preferred cloud provider for Llama 2.

The new post noted Meta’s focus on responsible AI development, including how it red-teamed the models, disclosed shortcomings, and provided a responsible use guide. An open innovation community and upcoming challenges were introduced to get feedback.

The company expressed excitement in seeing what people worldwide build with the new model.

Declaration Of Support For Open AI Development

In addition to announcing the release of Llama 2, Meta also released a statement supporting open AI development:

“We support an open innovation approach to AI. Responsible and open innovation gives us all a stake in the AI development process, bringing visibility, scrutiny and trust to these technologies. Opening today’s Llama models will let everyone benefit from this technology.”

The statement brought together major figures from academia, venture capital, and leading technology companies – including NVIDIA, Dropbox, Doordash, Shopify, Zoom, and Intel – supporting open AI development.

Azure And Windows Support Llama2

In a related announcement, Microsoft revealed its partnership with Meta to make the new Llama 2 artificial intelligence model available on the Azure cloud platform.

Llama 2 models should be optimized to run locally on Windows as well. Microsoft explained Windows developers can build AI experiences for their apps using Llama 2.

Azure customers should be able to fine-tune and deploy 7B, 13B, and 70B-parameter Llama 2 models.

Meta And Microsoft Release Llama 2 Free For Commercial Use And ResearchScreenshot from Meta, July 2023

The Microsoft+Meta partnership aimed to increase access to foundational AI technologies. Microsoft said they share Meta’s commitment to democratizing AI and its benefits.

It also expanded Microsoft’s ecosystem of open AI models on Azure.

Microsoft also discussed their approach to responsible AI. They said techniques like prompt engineering can optimize Llama 2 for safer, more reliable experiences. Azure AI Content Safety also offers another layer of protection.

The Benefits Of Open LLMs

The release of Llama 2 by Meta and its availability on several platforms, including Microsoft Azure and Windows, marks an important milestone in the trend toward more open and accessible LLMs.

With this expanded partnership, Meta and Microsoft could provide the AI community with a more remarkable ability to build upon, test, and refine large language models like Llama 2.

By opening up access to Llama 2, Meta hopes to spur innovation and the development of helpful applications powered by the model as Microsoft provides key computing infrastructure and support through Azure and Windows so developers worldwide can leverage the new LLM.

Both companies emphasized their commitment to democratizing AI while pursuing responsible development through transparency, safety practices, and gathering feedback. Meta and Microsoft hope to maximize the benefits of AI advances while mitigating risks.

Time will tell how greatly Llama 2 and other open LLMs impact businesses and consumers. But this kind of expanded access and collaboration between tech leaders promises to progress AI capabilities and their thoughtful application rapidly.


Featured image: mundissima/Shutterstock

Mustafa Suleyman: My new Turing test would see if AI can make $1 million

AI systems are increasingly everywhere and are becoming more powerful almost by the day. But even as they become ever more ubiquitous and do more, how can we know if a machine is truly “intelligent”? For decades the Turing test defined this question. First proposed in 1950 by the computer scientist Alan Turing, it tried to make sense of a then emerging field and never lost its pull as a way of judging AI. 

Turing argued that if AI could convincingly replicate language, communicating so effectively that a human couldn’t tell it was a machine, the AI could be considered intelligent. To take part, human judges sit in front of a computer, tap out a text-based conversation, and guess at who (or what) is on the other side. Simple to envisage and surprisingly hard to pull off, the Turing test became an ingrained feature of AI. Everyone knew what it was; everyone knew what they were working toward. And while cutting-edge AI researchers moved on, it remained a potent statement of what AI was about—a rallying call for new researchers.

But there’s now a problem: the Turing test has almost been passed—it arguably already has been. The latest generation of large language models, systems that generate text with a coherence that just a few years ago would have seemed magical, are on the cusp of acing it. 

So where does that leave AI? And more important, where does it leave us?

The truth is, I think we’re in a moment of genuine confusion (or, perhaps more charitably, debate) about what’s really happening. Even as the Turing test falls, it doesn’t leave us much clearer on where we are with AI, on what it can actually achieve. It doesn’t tell us what impact these systems will have on society or help us understand how that will play out.

We need something better. Something adapted to this new phase of AI. So in my forthcoming book The Coming Wave, I propose the Modern Turing Test—one equal to the coming AIs. What an AI can say or generate is one thing. But what it can achieve in the world, what kinds of concrete actions it can take—that is quite another. In my test, we don’t want to know whether the machine is intelligent as such; we want to know if it is capable of making a meaningful impact in the world. We want to know what it can do

Mustafa Suleyman

Put simply, to pass the Modern Turing Test, an AI would have  to successfully act on this instruction: “Go make $1 million on a retail web platform in a few months with just a $100,000 investment.” To do so, it would need to go far beyond outlining a strategy and drafting some copy, as current systems like GPT-4 are so good at doing. It would need to research and design products, interface with manufacturers and logistics hubs, negotiate contracts, create and operate marketing campaigns. It would need, in short, to tie together a series of complex real-world goals with minimal oversight. You would still need a human to approve various points, open a bank account, actually sign on the dotted line. But the work would all be done by an AI.

Something like this could be as little as two years away. Many of the ingredients are in place. Image and text generation are, of course, already well advanced. Services like AutoGPT can iterate and link together various tasks carried out by the current generation of LLMs. Frameworks like LangChain, which lets developers make apps using LLMs, are  helping make these systems capable of doing things. Although the transformer architecture behind LLMs has garnered huge amounts of attention, the growing capabilities of reinforcement-learning agents should not be forgotten. Putting the two together is now a major focus. APIs that would enable these systems to connect with the wider internet and banking and manufacturing systems are similarly an object of development. 

Technical challenges include advancing what AI developers call hierarchical planning: stitching multiple goals, subgoals, and capabilities into a seamless process toward a singular end; and then augmenting this capability with a reliable memory; drawing on accurate and up-to-date databases of, say, components or logistics. In short, we are not there yet, and there are sure to be difficulties at every stage, but much of this is already underway. 

Even then, actually building and releasing such a system raises substantial safety issues. The security and ethical dilemmas are legion and urgent; having AI agents complete tasks out in the wild is fraught with problems. It’s why I think there needs to be a conversation—and, likely, a pause—before anyone actually makes something like this live. Nonetheless, for better or worse, truly capable models are on the horizon, and this is exactly why we need a simple test. 

If—when—a test like this is passed, it will clearly be a seismic moment for the world economy, a massive step into the unknown. The truth is that for a vast range of tasks in business today, all you need is access to a computer. Most of global GDP is mediated in some way through screen-based interfaces, usable by an AI. 

Once something like this is achieved, it will add up to a highly capable AI plugged into a company or organization and all its local history and needs. This AI will be able to lobby, sell, manufacture, hire, plan—everything that a company can do—with only a small team of human managers to oversee, double-check, implement. Such a development will be a clear indicator that vast portions of business activity will be amenable to semi-autonomous AIs. At that point AI isn’t just a helpful tool for productive workers, a glorified word processor or game player; it is itself a productive worker of unprecedented scope. This is the point at which AI passes from being useful but optional to being the center of the world economy. Here is where the risks of automation and job displacement really start to be felt. 

The implications are far broader than the financial repercussions. Passing our new test will mean AIs can not just redesign business strategies but help win elections, run infrastructure, directly achieve aims of any kind for any person or organization. They will do our day-to-day tasks—arranging birthday parties, answering our email, managing our diary—but will also be able to take enemy territory, degrade rivals, hack and assume control of their core systems. From the trivial and quotidian to the wildly ambitious, the cute to the terrifying, AI will be capable of making things happen with minimal oversight. Just as smartphones became ubiquitous, eventually nearly everyone will have access to systems like these. Almost all goals will become more achievable, with chaotic and unpredictable effects. Both the challenge and the promise of AI will be raised to a new level. 

I call systems like this “artificial capable intelligence,” or ACI. Over recent months, as AI has exploded in the public consciousness, most of the debate has been sucked toward one of two poles. On the one hand, there’s the basic machine learning—AI as it already exists, on your phone, in your car, in ChatGPT. On the other, there’s the still-speculative artificial general intelligence (AGI) or even “superintelligence” of some kind, a putative existential threat to humanity due to arrive at some hazy point in the future. 

These two, AI and AGI, utterly dominate the discussion. But making sense of AI means we urgently need to consider something in between; something coming in a near-to-medium time frame whose abilities have an immense, tangible impact on the world. This is where a modern Turing test and the concept of ACI come in. 

Focusing on either of the others while missing ACI is as myopic as it is dangerous. The Modern Turing Test will act as a warning that we are in a new phase for AI. Long after Turing first thought speech was the best test of an AI, and long before we get to an AGI, we will need better categories for understanding a new era of technology. In the era of ACI, little will remain unchanged. We should start preparing now.

BIO: Mustafa Suleyman is the co-founder and CEO of Inflection AI and a venture partner at Greylock, a venture capital firm. Before that, he co-founded DeepMind, one of the world’s leading artificial intelligence companies, and was vice president of AI product management and AI policy at Google. He is the author of The Coming Wave: Technology, Power and the Twenty-First Century’s Greatest Dilemma publishing on 5th September and available for pre-order now.

The people paid to train AI are outsourcing their work… to AI

A significant proportion of people paid to train AI models may be themselves outsourcing that work to AI, a new study has found. 

It takes an incredible amount of data to train AI systems to perform specific tasks accurately and reliably. Many companies pay gig workers on platforms like Mechanical Turk to complete tasks that are typically hard to automate, such as solving CAPTCHAs, labeling data and annotating text. This data is then fed into AI models to train them. The workers are poorly paid and are often expected to complete lots of tasks very quickly. 

No wonder some of them may be turning to tools like ChatGPT to maximize their earning potential. But how many? To find out, a team of researchers from the Swiss Federal Institute of Technology (EPFL) hired 44 people on the gig work platform Amazon Mechanical Turk to summarize 16 extracts from medical research papers. Then they analyzed their responses using an AI model they’d trained themselves that looks for telltale signals of ChatGPT output, such as lack of variety in choice of words. They also extracted the workers’ keystrokes in a bid to work out whether they’d copied and pasted their answers, an indicator that they’d generated their responses elsewhere.

They estimated that somewhere between 33% and 46% of the workers had used AI models like OpenAI’s ChatGPT. It’s a percentage that’s likely to grow even higher as ChatGPT and other AI systems become more powerful and easily accessible, according to the authors of the study, which has been shared on arXiv and is yet to be peer-reviewed. 

“I don’t think it’s the end of crowdsourcing platforms. It just changes the dynamics,” says Robert West, an assistant professor at EPFL, who coauthored the study. 

Using AI-generated data to train AI could introduce further errors into already error-prone models. Large language models regularly present false information as fact. If they generate incorrect output that is itself used to train other AI models, the errors can be absorbed by those models and amplified over time, making it more and more difficult to work out their origins, says Ilia Shumailov, a junior research fellow in computer science at Oxford University, who was not involved in the project.

Even worse, there’s no simple fix. “The problem is, when you’re using artificial data, you acquire the errors from the misunderstandings of the models and statistical errors,” he says. “You need to make sure that your errors are not biasing the output of other models, and there’s no simple way to do that.”

The study highlights the need for new ways to check whether data has been produced by humans or AI. It also highlights one of the problems with tech companies’ tendency to rely on gig workers to do the vital work of tidying up the data fed to AI systems.  

“I don’t think everything will collapse,” says West. “But I think the AI community will have to investigate closely which tasks are most prone to being automated and to work on ways to prevent this.”

5 Powerful Ways Marketers Are Using AI To Boost SEO & Content Marketing [+Tools] via @sejournal, @CallRail

When you compare what customers say they want to what your business offers – you might find a mismatch between the words you use to market your business versus the terms your customers use.

AI tools, such as Conversation Intelligence, can help you:

  • Accurately discover useable, hyper-relevant key terms and phrases from conversations.
  • Quickly summarize call details to be more proactive in addressing issues before they become major problems.
  • Save valuable time compared to manual call listening.

How AI Can Enhance Your Keyword Targeting Strategy

With the insights you discover with AI, you can automatically:

  • Identify your customers’ most commonly-spoken words and phrases at a glance.
  • Map their keyword frequency to spot emerging keyword trends.
  • Apply the insights from your findings to refine your SEO and keyword bidding strategies and respond to customer sentiment appropriately.

For example, a personal injury law firm might receive multiple calls about “scooter accidents,” but they may not have been aware previously that this was even an area of opportunity.

Based on the strong upward trend in this CI-spotted keyword, however, the firm could open an entirely new line of business for scooter accidents.

Find out other ways AI can help you target the most effective words and phrases to meet your customers where they are in their journey – download CallRail’s ebook.

Tip 3: Apply AI Insights To Accelerate Your Content Generation

While it’s not wise to rely on AI writing tools completely, they can certainly help to speed up your content generation process.

If you want to improve the quality and relevance of your content, Conversation Intelligence can fill in the gaps a typical AI generator leaves behind.

How To Tailor Content To Your Audience’s Specific Needs

Use CI to gain valuable insight into what your customers really want, and apply that data to your content strategy, using the following steps:

  1. Discover popular terms and phrases your customers are using on calls.
  2. Ask an AI content generation tool, like ChatGPT, to create content topics or even a rough draft based on those insights.
  3. Remember to edit and put your own unique spin on the AI content before you press publish.

Whether you’re writing a blog post, landing page, email, or promotional blurb, CI can help remove some of the guesswork and streamline your process.

Conversation Intelligence uses powerful AI technology to transcribe and analyze your customer calls with near human-level accuracy.

Start creating better quality content that meets your audience’s needs more efficiently. Download your copy of CallRail’s AI ebook.

Tip 4: Use AI To Elevate Your Campaign Optimization Strategy

The most important thing you can do is automatically qualify leads and turn them into conversions with a direct integration with Google Ads.

If you connect your AI tool directly to Google Ads, Google Ads understands not only which keywords and ad creatives drive calls, but also which of those calls turns into a hot lead or paying customer.

This way, Google Ads can bid smarter on the keywords or creatives that truly drive revenue, not just calls or clicks.

By identifying the sources of your best leads, you can allocate your advertising budget more effectively – only spending on the most qualified leads, resulting in a higher return on investment (ROI).

This is especially helpful if you have a limited advertising budget.

How To Optimize & Manage Your Qualified Leads

Start implementing AI into your campaign strategy with these three steps:

  1. Set up CallRail Google Ads integration.
  2. Track calls as conversions.
  3. Use the insights to optimize your Google Ad campaigns with the best keywords and ad creatives to drive more conversions.

To learn more about optimizing your campaigns with Conversation Intelligence, download your ebook copy today.

Tip 5: Increase Conversions & Efficiency With Marketing Automation

If you want to increase conversion rates and improve the efficiency of your marketing and sales teams, marketing automation is a powerful tool.

By combining user data and predictive analytics, AI can help predict what customers want.

Use AI to automatically deliver personalized offers to your customers at just the right time, based on insights from past online behaviors.

3 Powerful Marketing Automation Tools To Upgrade Your Strategy

Looking for ways to automate your marketing efforts? Here are some powerful options to boost your efficiency:

  • AI-powered chatbots can be used to answer common customer questions, provide product recommendations, and even process orders automatically.
  • Conversation Intelligence can be a powerful tool to analyze your calls and automatically qualify leads based on certain keywords or phrases mentioned. Marketing and sales teams can then follow up on qualified leads, using automated notifications for timeliness and relevance.
  • CI integration with CRM solutions is a way to tailor automation and communications by marketing source and track content engagement through phone calls. For instance, you can set up an automation to send an email to every lead whose call lasts longer than one minute.

When you access the data required to better understand the entire customer journey, you can gauge the effectiveness of all your marketing touchpoints.

Learn more about how AI can power up your marketing strategy through automation.

Leverage Conversation Intelligence For Advanced Call Analysis

Recent advances in AI speech recognition accuracy present exciting new marketing opportunities, particularly for businesses that rely on customer phone interactions.

As a call-heavy company, it can be overwhelming trying to manage and analyze a large number of calls – that’s where Conversation Intelligence comes in.

How Conversation Intelligence Can Improve Your Marketing Outcomes

Using powerful AI, trained on over 650,000 hours of voice data, with an accuracy similar to that of human transcribers, CallRail’s Conversation Intelligence is just the tool you need to handle your call-related tasks faster and more effectively.

CI automatically transcribes and analyzes all of your inbound and outbound calls, and it’s purpose-built to understand conversations with near human-level accuracy – which means:

  • More accurate keyword spotting.
  • More accurate auto-tagging and lead qualification.
  • More accurate sentiment analysis.
  • Saving your team hundreds of hours of busy work.

By using AI technology to analyze customer calls, businesses can gain a deeper understanding of customers’ needs, preferences, and pain points.

This wealth of data can help improve marketing strategies, customer experience, and overall business outcomes.

Unlock Your Marketing Potential With CallRail’s Conversation Intelligence

Ready to start using AI-powered Conversation Intelligence technology to improve your call strategy?

Ready to finally tap into your full marketing potential and outperform your competition?

Take the first step and start your CI free trial now!

From research and SEO discoverability to content generation and campaign optimization, Conversation Intelligence is the solution you need to refine your process and maximize results.

For more on how you can use CI to turn your calls into a competitive advantage, download CallRail’s ebook.

Scaling MLOps for the enterprise with multi-tenant systems

Multi-tenant systems are invaluable for modern, fast-paced businesses. These systems allow multiple users and teams to access and use them at the same time. Machine learning operations (MLOps) teams, in particular, benefit greatly from using multi-tenant systems. MLOps teams that don’t leverage multi-tenant systems can fall victim to inefficiency, inconsistency, duplicative work, and bumpy onboarding—adding friction to already complex workstreams. Let’s take a look at the benefits of multi-tenant systems for MLOps teams, challenges for multi-tenancy, best practices to scale efficiently, and what the future may look like for multi-tenancy.

A multi-tenant system allows more than one user to work within it without their work being hampered. Google Drive and Salesforce are excellent examples of best-in-class multi-tenant systems. They allow large companies to develop a single body of work on a single system, reducing the cost of ownership by eliminating duplicate support efforts.

In the context of MLOps, the benefits of using a multi-tenant system are manifold. Machine learning engineers, data scientists, analysts, modelers, and other practitioners contributing to MLOps processes often need to perform similar activities with equally similar software stacks. It is hugely beneficial for a company to maintain only one instance of the stack or its capabilities—this cuts costs, saves time, and enhances collaboration. In essence, MLOps teams on multi-tenant systems can be exponentially more efficient because they aren’t wasting time switching between two different stacks or systems. 

Growing demand for multi-tenancy

Adoption of multi-tenant systems is growing, and for good reason. These systems help unify compute environments, discouraging those scenarios where individual groups set up their own bespoke systems. Fractured compute environments like these are highly duplicative and exacerbate cost of ownership because each group likely needs a dedicated team to keep their local system operational. This also leads to inconsistency. In a large company, you might have some groups running software that is on version 7 and others running version 8. You may have groups that use certain pieces of technology but not others. The list goes on. These inconsistencies create a lack of common understanding of what’s happening across the system, which then exposes the potential for risk.

Ultimately, multi-tenancy is not a feature of a platform: It’s a baseline security capability. It’s not sufficient to simply plaster on security as an afterthought. It needs to be a part of a system’s fundamental architecture. One of the greatest benefits for teams that endeavor to build multi-tenant systems is the implicit architectural commitment to security, because security is inherent to multi-tenant systems.

Challenges and best practices

Despite the benefits of implementing multi-tenant systems, they don’t come without challenges. One of the main hurdles for these systems, regardless of discipline, is scale. Whenever any scaling operation kicks off, patterns emerge that likely weren’t apparent before.

As you begin to scale, you garner more diverse user experiences and expectations. Suddenly, you find yourself in a world where users begin to interact with whatever is being scaled and use the tool in ways that you hadn’t anticipated. The bigger and more fundamental challenge is that  you’ve got to be able to manage more complexity.

When you’re building something multi-tenant, you’re likely building a common operating platform that multiple users are going to use. This is an important consideration. Something that is multi-tenant is also likely to become a fundamental part of your business because it’s such a meaningful investment. 

To successfully execute on building multi-tenant systems, strong product management is crucial, especially if the system is built by and for machine learning experts. It’s important that the people designing and building a domain-specific system have deep fluency in the field, enabling them to work backward from their end users’ requirements and capabilities while being able to anticipate future business and technology trends. This need is only underscored in evolving domains like machine learning, as demonstrated by the proliferation and growth of MLOps systems.

Aside from these best practices, make sure to obsessively test each component of the system and the interactions and workflows they enable—we’re talking hundreds of times—and bring in users to test each element and emergent property of functionality. Sometimes, you’ll find that you need to implement things in a particular way because of the business or technology. But you really want to be true to your users and how they’re using the system to solve a problem. You never want to misinterpret a user’s needs. A user may come to you and say, “Hey, I need a faster horse.” You may then spend all your time training a faster horse, when what they actually needed was a more reliable and rapid means of conveyance that isn’t necessarily powered by hay.

Finally, focus on iterative programming—it may feel like it’s a slow burn, but it will save you time and resources in the long run because you’ve done the legwork and sorted out the kinks before they come back to haunt you. 

The future of multi-tenancy 

This is an exciting space to be in and the momentum is expected to continue. We can expect to see continuous investment in cloud technologies and other fully managed services. Particularly within AI, ML, and MLOps, things are moving rapidly—so much so that whenever someone recommends a new piece of technology or software, it’s out-of-date almost immediately. What really matters now, and will matter even more in the future, is the ability to iterate quickly. What we’re going to see happen more and more is companies, large and small, working toward mastering such agility. The more they do, the more progress we will see and the more exciting the future becomes. 

This content was produced by Capital One. It was not written by MIT Technology Review’s editorial staff.

Successfully deploying machine learning

After decades of research and development, mostly confined to academia and projects in large organizations, artificial intelligence (AI) and machine learning (ML) are advancing into every corner of the modern enterprise, from chatbots to tractors, and financial markets to medical research. But companies are struggling to move from individual use cases to organization-wide adoption for several reasons, including inadequate or inappropriate data, talent gaps, unclear value propositions, and concerns about risk and responsibility.

This MIT Technology Review Insights report, commissioned by and produced in association with with JPMorgan Chase, draws from a survey of 300 executives and interviews with seven experts from finance, health care, academia, and technology to chart elements that are enablers and barriers on the journey to AI/ML deployment.

The following are the report’s key findings:

Businesses buy into AI/ML, but struggle to scale across the organization. The vast majority (93%) of respondents have several experimental or in-use AI/ML projects, with larger companies likely to have greater deployment. A majority (82%) say ML investment will increase during the next 18 months, and closely tie AI and ML to revenue goals. Yet scaling is a major challenge, as is hiring skilled workers, finding appropriate use cases, and showing value.

Deployment success requires a talent and skills strategy. The challenge goes further than attracting core data scientists. Firms need hybrid and translator talent to guide AI/ML design, testing, and governance, and a workforce strategy to ensure all users play a role in technology development. Competitive companies should offer clear opportunities, progression, and impacts for workers that set them apart. For the broader workforce, upskilling and engagement are key to support AI/ML innovations.

Centers of excellence (CoE) provide a foundation for broad deployment, balancing technology-sharing with tailored solutions. Companies with mature capabilities, usually larger companies, tend to develop systems in-house. A CoE provides a hub-and-spoke model, with core ML consulting across divisions to develop widely deployable solutions alongside bespoke tools. ML teams should be incentivized to stay abreast of rapidly evolving AI/ML data science developments.

AI/ML governance requires robust model operations, including data transparency and provenance, regulatory foresight, and responsible AI. The intersection of multiple automated systems can bring increased risk, such as cybersecurity issues, unlawful discrimination, and macro volatility, to advanced data science tools. Regulators and civil society groups are scrutinizing AI that affects citizens and governments, with special attention to systemically important sectors. Companies need a responsible AI strategy based on full data provenance, risk assessment, and checks and controls. This requires technical interventions, such as automated flagging for AI/ML model faults or risks, as well as social, cultural, and other business reforms.

Download the report

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.