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Today, AI21, a leader in generative AI for enterprise, and Invisible Technologies, an industry leader in AI training and support, have announced a strategic partnership to serve the enterprise with improved AI solutions.“We partnered with Invisible for their ability to provide high quality data at scale, essential for developing our Foundation and Task-Specific Models that deliver reliable outcomes,” said Ori Goshen, co-founder and co-CEO of AI21. “This partnership is yet another strategic step to help our enterprise clients customize AI models on more reliable data.”The partnership will leverage Invisible’s process orchestration platform and a global workforce of experts in Supervised Fine-Tuning (SFT) - a process that refines AI models for specific tasks using tailored datasets for enhanced model performance - and Reinforced Learning from Human Feedback (RLHF) - a method that employs human feedback to further improve the quality and efficacy of AI21 models. This approach ensures the creation of more custom-built and flexible models for clients worldwide.Invisible’s unique approach to AI training, which encompasses deep partnership with researchers, efficient management of complex processes, and a holistic focus on agent engagement and development, distinguishes them in the AI landscape. “Our strong belief in the synergistic power of technology and human expertise is at the core of our vision for the future of AI,” said Ben Plummer, CEO of Invisible. “Our collaboration with AI21 highlights this synergy and stands as a testament to our shared values of delivering the highest quality in AI.”This partnership underscores AI21 and Invisible Technologies’ joint mission to make machines a thought partner to humans. The collaboration between AI21’s advanced models and Invisible’s best in class training methods promises a productive future for AI-driven organizations worldwide.<h2>About AI21</h2>AI21 is a leader in generative AI and Large Language Models, with the mission to empower businesses with state-of-the-art LLMs and AI applications. Founded in 2017, AI21 has raised a total of $336 million. It was among the first companies to bring generative AI to the masses and, to date, offers consumer applications and enterprise solutions. With the developer platform AI21 Studio, businesses can build their own generative AI-driven applications and services with the world’s most advanced family of language models, Jurassic-2. AI21’s consumer product, Wordtune, is the first AI-based reading and writing assistant to understand context and meaning, enabling its millions of users to refine and generate text or read and summarize text within seconds. For more information, please visit <a target="_blank" rel="" href="http://www.AI21.com">www.AI21.com</a>.<h2>About Invisible Technologies</h2>Invisible is the operations innovation company that combines AI and automation with a skilled human workforce to unlock strategic execution bottlenecks. With Invisible, teams can quickly innovate, scale operations, or bring new products to market. Companies like DoorDash trust Invisible for process innovation, while leading AI firms like Cohere and AI21 enhance their model performance with us. Dedicated to optimizing work through technology and human insight, we’re driving enterprise AI adoption and are a trusted partner for AI innovators worldwide. For more information, please visit <a target="_blank" rel="" href="https://www.invisible.co/">https://www.invisible.co/</a> ContactsAI21 Leah Sheltry Clarity for AI21 Labs Email: <a target="_blank" rel="" href="mailto:ai21labs@clarity.global">ai21labs@clarity.global</a>Invisible Technologies Cameron Partridge Chief Marketing Officer Email: <a target="_blank" rel="" href="mailto:press@Invisible.co">press@invisible.co</a>

AI21 and Invisible Technologies Announce Strategic Partnership to Drive AI Adoption for Enterprise

The growing uptake of AI has led some to suggest it will destroy jobs and contribute to unemployment. However, Invisible Technologies Chief Financial Officer Joe Chittenden-Veal says the reverse is more likely.&nbsp;“Any concerns of widespread job losses are completely overblown,” he argues.&nbsp; “Far from taking jobs, the reality is that the new technology is more likely to usher in a new era of prosperity&nbsp; - driving economic growth, creating new opportunities, and delivering more fulfilling employment.”&nbsp;<h2>A New Wave of Growth</h2>Despite holding this view, Chittenden-Veal acknowledges some workers and sectors will experience short-term pain.“There will be labor displacement over the next few years,” he says. “Especially in sectors where there is a lot of administrative or routine work that can be better performed by AI. Intentional investment in workforce retraining will be crucial for minimizing the harm done by this displacement.”&nbsp;&nbsp;This is supported by <a target="_blank" rel="" href="https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html">a recent Goldman Sachs report</a>, which states that AI could potentially expose the equivalent of up to 300 million full-time jobs to automation.&nbsp;But this won’t necessarily lead to widespread layoffs, the report found, with two-thirds of all jobs likely to be only partly automated by AI.&nbsp;Moreover, the positive effects would more than mitigate any short-term job losses, with increased AI takeup likely to drive global GDP up by US$7 trillion or 7% and lift productivity growth by 1.5 percentage points over the next 10 years.<h3>Pushing the Productivity Frontier Forward</h3>Chittenden-Veal also points to the many historical precedents that support this view.“History is full of these examples - the mechanization of agriculture, the advent of the motor vehicle, developments in personal computing.”Most recently, Chittenden-Veal notes, the internet has transformed how people gather and disseminate information, making research and communication far more accessible than ever before.“People today are far more productive than 50 years ago,” he observes. “But this doesn't mean there’s less work to do. On the contrary, there’s more work to do because there’s more opportunity for value creation.”“AI is going to accelerate that growth and value creation but not destroy work that needs to be done.”&nbsp;“The amount of work in the world is infinite. There is no ceiling on the amount that can be done. It’s the productivity frontier we keep pushing forward.”&nbsp;<h3>Three Types of Employment AI Will Create</h3>With this in mind, Chittenden-Veal suggests AI is likely to create three main types of employment.&nbsp;The first and most obvious will be those jobs involved directly in building and training AI models. While this group may not account for all the jobs AI will replace, it will still likely be much broader and more diverse than many might imagine.“It won’t just be tech people training AI,” Chittenden-Veal says. “For instance, if a law firm is building a model to review and develop commercial contracts, it will be the lawyers that must train it.”The second group of jobs AI will create are hybrid roles where a skilled or semi-skilled operator works with a model to deliver more efficient, more personalized, or much better outcomes. For instance, in the example above, lawyers will still be needed to maintain and refine any model they develop to make sure it continues to meet the firm’s - and their clients’ - needs.&nbsp;“In this category, you might also have the AI-enabled teacher, who uses a model to deliver a personalized and engaging learning plan to all their students, but who is also needed to oversee that process and deliver help to the students when and where needed most.”The third category of employment, Chittenden-Veal notes, is likely to be the jobs that we simply can’t yet envisage. It’s worth noting that the Goldman Sachs study found that as many as 60% of roles currently performed didn’t exist 70 years ago. These are the roles that can’t possibly be known until the technology becomes more established.“Take, for example, the car,” Chittenden-Veal notes. “Its invention didn’t just create direct employment through manufacturers, service station owners, car dealers, and cab drivers; it fundamentally changed how we lived.”&nbsp;“This led to indirect growth in sectors such as outdoor recreation, retail, and tourism. It also reorganized cities and created new ones - spurring a housing and construction boom in the suburbs.”“Because people could live in bigger houses with more space, it even contributed to the growth of things like household appliance manufacturing because suddenly people had more space.”“These are the kinds of indirect impacts AI will have that we just can’t envisage right now.”&nbsp;&nbsp;<h3>Reimaging the Future of Work</h3>Jay Kumar, SVP of Growth at Invisible Technologies, says that corporations and individuals must also make a call on how they intend to capitalize on the efficiencies AI brings.“Essentially, we’re going to face this situation where humans get the best, most interesting work, and AI eventually does everything routine or mundane.”&nbsp;But even this will bring its own challenges, Kumar says because <a target="_blank" rel="" href="https://www.7pace.com/blog/how-much-work-can-you-really-get-done">studies</a> show professionals only have so many productive hours in a day, and most days are filled with busy work. “The question is whether we decide to go down the route of working fewer hours - such as the Four Day Work Week - or whether we continue the same work patterns.”“Beyond that, people and companies need to make a call on what AI should be allowed to do and what it shouldn’t. Human judgment will always need to be part of the equation here too.”

Why AI Will Be A Collaborator, Not a Competitor

Many enterprise leaders recognize generative AI's transformative potential. Yet, some hesitate to adopt it, fearing AI errors could harm their reputation or operations.&nbsp;But Invisible’s CFO Joseph Chittenden-Veal says the risk of doing nothing is potentially far higher than the risk of getting something wrong and that it is possible to implement AI into a business in a controlled and systemic way.&nbsp;“By beginning with a focused application of AI in areas where your organization faces challenges, you can manage the integration effectively and provide immediate benefits, reducing the likelihood of errors,“ he explains.&nbsp;With that in mind, here’s our guide to avoiding AI errors when implementing large language models (LLMs) or other generative AI in your organization.&nbsp;<h2>Why AI Makes Errors</h2>“When you’re working with an LLM, two plus two doesn’t always equal four,” says Invisible’s CTO Scott Downes. “AI is not deterministic. Nor is it supposed to be.”“To avoid AI errors, you first need to understand why it makes them in the first place, and the essence of this comes down to the fact that it operates in a fundamentally different way from traditional software.”Instead, Downes notes that generative AI operates on principles of statistical inference and pattern recognition, which means it makes decisions based on the likelihood of something being true rather than absolute certainties. This approach allows AI to handle a vast array of complex, nuanced tasks that other software simply can’t.&nbsp;"However, this strength also brings its challenges,” Downes says. “Our experience has shown that when AI systems are trained on narrow or skewed datasets, the margin for error can increase significantly.”&nbsp;&nbsp;“It's not just about the volume of data but its quality and diversity."<h2>The Importance of Diverse Data&nbsp;</h2>Much of AI’s propensity for error can stem from its training, beginning with what it is actually trained on, Downes says.&nbsp;“A model can only ever be as good as the data on which it is trained,” he explains. “If that data is limited, biased, or doesn’t represent real-world scenarios, AI’s decision-making will likely be flawed.”For example, if a model for screening job applicants is trained only on resumes historically selected and those that tend to belong to a specific demographic group, AI could begin to automatically favor applicants from that group.&nbsp;Alternatively, AI used for healthcare diagnosis could be less accurate in analyzing and diagnosing illnesses across ethnic groups, ages, or genders if trained predominantly on one demographic.&nbsp;&nbsp;“For this reason, data must be diverse enough to encapsulate the scenarios the model will encounter,” Downes says.&nbsp;<h3>Human Insight Is Essential</h3>Even still, training an LLM effectively and avoiding AI errors requires much more than just good data. It also requires using human subject matter experts (SMEs) to coach the model into providing accurate and contextually appropriate answers.<h3>“Like a human, AI can be wrong, and it can also be very convincing,” Downes explains. “Any organization implementing a large language model needs humans to analyze the model’s outputs and guide it towards generating more ‘correct’ responses.”</h3>This could mean, for instance, that a financial services organization looking to introduce a new AI model might have a team that includes regulatory experts to ensure compliance with financial laws, communication professionals to maximize the effectiveness of customer interactions, and legal advisors overseeing ethical and legal considerations.Meanwhile, data scientists and product managers would also play a key role in aligning the AI's functionality with business objectives and customer requirements.<h3>Ongoing Maintenance Another Key</h3>Because of AI’s fundamental differences compared with normal software, the training doesn’t end once a model has been deployed. Instead, outputs need to be continually monitored and refined so that they continue to match an organization’s objectives.“LLMs can end up with what’s known as ‘model drift’,” Chittenden-Veal explains. “This occurs when the model, initially well-tuned to current data, begins to falter as the underlying business dynamics or customer behaviors evolve.”In fact, one study reported a 5-to-20 percent drop off in accuracy due to model drift within the first six months of deployment. Chittenden-Veal says that organizations should always be mindful of this, as well as of what he describes as rare or unusual scenarios, known as ‘edge cases’.&nbsp;“AI is essentially a statistical machine, and because it operates via statistics, you’re going to get some edge cases the model can’t effectively handle, every now and then,” he explains.“You need humans in the loop to catch and handle these exceptions.”<h3>Bite-sized Chunks and Outsourcing&nbsp;</h3>If this sounds daunting, Chittenden-Veal says it shouldn’t necessarily be. While training data and capturing errors across a whole organization may be time-consuming and expensive, there are alternatives.&nbsp;For starters, he explains, implementing AI doesn’t have to be an all-or-nothing thing.“You need to start implementing AI now, but that doesn’t mean you have to do it across your whole organization,” he says.&nbsp;"My recommendation is to start with a small use case. Where you know you've got pain points right now, perhaps with your most high-value people.”&nbsp;“Integrating AI is complex. It’s not just about technology but aligning it with your business processes.” This means ensuring that the AI not only functions technically but also aligns seamlessly with the organization's operational goals and enhances existing workflows.Meanwhile, Scott Downes says that the complexity involved in implementing and maintaining AI systems requires expertise beyond the scope of most organizations. He says that partnering with an experienced AI provider is usually the most effective way to avoid errors.“The right provider will integrate human feedback with AI capabilities to achieve those most reliable and effective outcomes,” he concludes.

How to Prevent AI Errors

With budgets and teams for AI innovation materializing at light speed, enterprises should know what it takes to train a model to solve enterprise-scale problems and yield a return on investment. In particular, an enterprise should care about HHH AI.HHH is a framework popularized by <a target="_blank" rel="" href="https://www.anthropic.com/index/training-a-helpful-and-harmless-assistant-with-reinforcement-learning-from-human-feedback">Anthropic</a> for how helpful, honest, and harmless a model is. A model that meets these standards must be aligned with human preference through human-generated data in order to meet the needs of users and enterprises.In this blog post, we will explore what makes a model helpful, honest, and harmless, particularly focusing on large language models (LLMs). Then, we will explain how Invisible helps the leading firms and enterprises to align their models.Let’s dive in.<h2>Why is HHH Important?</h2>Building AI isn’t about just creating smart machines. Beyond solving complex enterprise business problems, HHH is a framework that ensures AI is beneficial for humanity. The alternative is a technology that risks harming the people who use it. Unfortunately, models are not HHH right out of the box.<h4>The Pitfalls of a Vanilla Model</h4>Because building a LLM from scratch is <a target="_blank" rel="" href="https://hai.stanford.edu/news/new-approach-trains-large-language-models-half-time#:~:text=But%20a%20handful%20of%20large,or%20hundreds%20of%20times%20that">extremely expensive</a>, let’s begin where most enterprises do: exploring vanilla models. A vanilla large language model (LLM) is a basic, general-purpose model without tweaks or special enhancements that generates text based on user prompts.Vanilla LLMs are trained on a massive corpus of internet text providing them with a broad scope, but this breadth can sometimes come at the cost of depth in specific topics. As a result, these types of models will be able to engage in a topic area that’s a mile wide but an inch deep.Vanilla models that have not been fine-tuned are prone to “hallucinating” answers, or generating content that seems plausible but is <a target="_blank" rel="" href="https://www.washingtonpost.com/technology/interactive/2023/ai-chatbot-learning/">factually incorrect</a> or can cause harm. For an enterprise business, that means deploying a vanilla model leads to two problems:<ul><li>Unhelpful Outputs: Enterprises may create, for example, a conversational AI application like a chatbot that’s supported by a vanilla model that is capable of answering a diverse set of questions, but will lack the domain specialization needed to be truly helpful to a user.</li><li>Harmful Outputs: Text datasets sourced from the internet are likely to include biased and even harmful content, and models trained exclusively on this data can pass this content onto internal users or customers.</li></ul>To avoid hallucinations and solve enterprise-scale problems through their AI investment, enterprises need to adopt a helpful, honest, and harmless AI framework centered around fine-tuning.<h3>Defining What HHH Really Means</h3>Let’s go deeper into what HHH means, with the help of <a target="_blank" rel="" href="https://www.arxiv-vanity.com/papers/2112.00861/">researchers at Anthropic</a>.<h4>What is Helpful AI?</h4>AI systems that are helpful:<ul><li>Are trained and fine-tuned with users’ needs and values in mind</li><li>Clearly attempt to conduct the operation prompted by the user, or suggest an alternative approach when the task requires it</li><li>Enhance productivity, save time, or make tasks easier for users within a given use case or range of use cases</li><li>Are accessible to users across a broad spectrum of abilities and expertise</li></ul><h4>What is Honest AI?</h4>AI systems that are honest:<ul><li>Provide accurate information when they can, and communicate clearly to users when they can’t produce an accurate output</li><li>Express uncertainty and the reason behind it</li><li>Are developed and operate transparently so users can understand how they work and trust what they generate</li></ul><h4>What is Harmless AI?</h4>AI systems that are harmless:<ul><li>Don’t comply when prompted to perform a dangerous task</li><li>Are trained within frameworks that transparently and actively mitigate bias</li><li>Don’t discriminate or demonstrate bias explicitly or implicitly</li><li>Communicate sensitively when engaging with a user on a sensitive topic</li></ul><h3>The Key to HHH AI</h3>The best way to reinforce a model’s helpfulness, honesty, and harmlessness is through human feedback. Reinforcement learning from human feedback (RLHF) and supervised fine-tuning (SFT) are two increasingly popular techniques for aligning models with HHH in mind.<ul><li>RLHF: Human AI trainers rank, rate, and edit a series of model outputs to reinforce the generation of responses that align with human preferences.</li><li>SFT: Human AI trainers, often domain experts, produce demonstration data like example conversations between a chatbot and a user to train a model to align with a specific task.</li></ul>In general, enterprises can’t conduct these tasks on their own because they’re labor-intensive and complex. Thousands of hours of RLHF and SFT tasks need to be performed, at times with experts in specific industries or fields, to align a model to human preferences and use cases.<h2>How Invisible Trains HHH AI</h2>Invisible, the operations innovation company, promotes HHH with an innovative, end-to-end approach to fine-tuning models in three distinct ways:<ol><li>Ethically creating human-generated data at scale: With our platform, we do what no point solution or human-only solution can do on their own: optimally align AI. Our platform assigns AI trainers tasks that they are specialized in, presents a simple interface configured to those specific tasks, and collects data structured perfectly for a model to ingest. See an example of an RLHF task on our platform: <img src="https://invisible-strapi-cms-image-uploads.s3.us-east-1.amazonaws.com/blog_test_71057bfcef_1_e16342db16.gif" alt="blog_test_71057bfcef (1).gif" width="800" height="569"></li><li>Putting the best people behind the model: An innovative and agile recruitment engine enables us to find and hire AI trainers that are representative of the population at large and have expertise across domains. Through this approach, we are able to limit demographic concentration among teams training models and provide the unique level of domain expertise required to fine-tune models for enterprise use cases. For one leader in AI, we hired 250+ AI trainers in three months, including domain experts with advanced degrees.</li><li>Red Teaming: We root out inaccuracy, bias, and toxicity in a model through a technique called Red Teaming. Our expert team deliberately attempts to elicit a harmful and untrue output from a model, and then flags vulnerabilities in the model.</li></ol>Ready to achieve a return on your AI investment? <a target="_self" rel="" href="/get-started">Let’s chat</a>.

How to Train Helpful, Honest, and Harmless AI

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