We’re thrilled to unveil the newest version of our advanced digital assistant, Ida 23.02. With a series of transformative enhancements, crucial fixes, and forward-looking features, Ida 23.02 is poised to push the conversational UI to new levels. In this release, we’re particularly excited to highlight three key features that set a new benchmark in AI-assisted functionalities.
New Generative AI to Suggest FAQ Answers: This powerful feature is a game-changer in the realm of digital assistance. Our newly implemented generative AI technology takes Ida beyond just retrieving information—it predicts and suggests answers to your questions, significantly improving the efficiency of managing Ida.
New Generative AI Training Assistant: We understand the importance of training AI models efficiently, and our new generative AI training assistant is designed to make this process smoother. By assisting in the AI model training process, this feature enhances overall performance and results in more accurate, reliable predictions and responses while saving you tons of time.
New Ida Alerts Feature: Stay on top of important notifications and updates with our new Ida Alerts feature. This feature ensures your users are alerted to timely information and deadlines to drive your business processes.
Here’s a summarized list of all the features, enhancements, and fixes included in the Ida 23.02 release:
With these enhancements, we are reimagining what digital assistance can do. Join us on this exciting journey and explore the full potential of Ida 23.02 today! Contact us below to learn more and setup your own personal demo.
Paul Taylor and Andrew Bediz join Robbin Velayedam, Senior Director, Product Management – Oracle HCM, on this month’s PeopleSoft Chat podcast. The group talks about the Blueprint4D conference, artificial intelligence (AI) in the enterprise, and the factors to consider in a customers transition to cloud.
The podcast discussion covers a range of topics, including the challenges of getting users to adopt enterprise software, the importance of user experience, and the benefits of automation. The guests also discuss their experiences at Blueprint 4D, a PeopleSoft conference, and the future of PeopleSoft and enterprise software more broadly. They talk about the potential for integrating voice and conversation into enterprise software, as well as the benefits of cloud-based solutions. The podcast provides valuable insights into the challenges and opportunities of enterprise software and highlights the importance of user-centered design.
With the increasing popularity of AI, the issue of bias has become a significant concern. AI, after all, is designed to learn from data, and if the data it is trained on is biased, the AI will likely reflect that bias in its decisions.
For example, when ChatGPT (produced by OpenAI) first debuted, people quickly noticed that it seemed to prefer one presidential candidate over another. This raised questions about whether an AI could have a political preference, and if so, where that preference might come from.
AI is not some kind of magical, all-knowing entity. At the end of the day, it’s just a computer program. It doesn’t have emotions or opinions, and it’s only as good as the data it’s trained on. As they say, garbage in, garbage out.
Let’s take Jo, the Job Board Bot, as an example. Jo has been trained on resumes from people who have been hired for various positions. Based on this data, Jo can tell a candidate which job they’re most likely to succeed in based on their qualifications.
Now imagine Barbara launches Jo and asks, “What position am I best fit for?” Even if Barbara has all the relevant experience, if 84% of engineers are men, Jo might not suggest that Barbara would be a good fit for a computer engineer position. That’s not the outcome you’re looking for!
Now, let’s revisit the issue of bias in ChatGPT’s political preferences. ChatGPT relies on numerous public data sources, including Wikipedia, which is written by humans, and unpaid humans at that! Unfortunately, if these humans have any inherent biases in their writing, ChatGPT is likely to inherit those same biases. Essentially, ChatGPT’s biases reflect the collective biases of the content creators it relies on.
Update: ChatGPT has since placed filters on certain outputs like those discussed above and instead will respond that the AI doesn’t have preferences.
If AI is trained on biased data, it will produce biased outcomes, which can create a feedback cycle; for example, as Jo essentially screens out female applicants, the gender disparity increases, making new female applicants look even less likely to succeed. How do we break the cycle?
The common technique is called supervised training, which OpenAI does employ. Per the Wired article linked, “[Supervised training] …was used to enhance ChatGPT. It involves having humans judge the quality of the model’s answers to steer it towards providing responses more likely to be judged as high quality.” However, the supervisors are humans, so how do we keep the human supervisors from creating their own bias? Oh boy!
When implementing this technology in the enterprise, the stakes are significantly higher. Some AI products don’t even use your organization’s data. Some do, but they are not trained by your people. Instead, the AI provider has a team of their own people, many times in offshore centers, who are monitoring the AI’s decisions and providing feedback to supervise the training.
It is unlikely, then, that the vendor team teaching the AI has the same view of what is or isn’t appropriate as the people who will be using the AI. Knowing what you know now about bias in AI, do you think it is a good idea to have a team in a different part of the country, or another country, being the sole influencer of the AI?
At Gideon Taylor, we prioritize meeting the specific enterprise needs of organizations we work with. Our digital assistant, Ida, uses a language model from Oracle, a company with extensive experience handling enterprise data. However, we don’t stop there. We recognize that each customer’s needs and data are unique, so we train their bot independently, rather than relying on a one-size-fits-all approach.
We also understand the importance of our customers influencing their own AI. By allowing your people to provide feedback to Ida, we ensure that the AI is constantly learning and adapting to better represent your organization, your language, and your culture. If you haven’t seen Ida, contact us below and setup a personalized demo.
We are excited to announce the release of Ida version 23.01, packed with new features and enhancements to streamline your digital assistant. With a focus on improving user interactions and expanding data sources, this release brings exciting updates to Ida’s capabilities. Here’s a closer look at some highlights from Ida 23.01:
In addition to these major updates, Ida 23.01 also includes various bug fixes and performance improvements to enhance the overall stability and reliability of the digital assistant.
To upgrade to Ida 23.01, please contact your account team to discuss a deployment schedule that works best for your organization. We are committed to continuously improving Ida to meet the evolving needs of our users, and we look forward to your feedback on this exciting new release.
Thank you for your continued support, and we are confident that Ida 23.01 will further enhance your digital assistant experience.
Contact us below to learn more and setup your own personal demo
The biggest thing in tech the last few weeks has been GPT-3. GPT, or Generative Pre-trained Transformer, is a language model developed by OpenAI that uses deep learning to produce human-like text. It is based on the Transformer architecture, which was introduced by Google in 2017. GPT uses a self-attention mechanism to learn the context of a given sentence and generate text that is both coherent and natural sounding. It has been used to generate text for tasks such as summarization, dialog generation, machine translation, and more. GPT is a powerful tool for natural language processing and is one of the most widely used language models today.
What if I told you that the previous paragraph was not written by me, but by a machine? That “wow!” moment you just had is one of many to come, as we realize how powerful artificial intelligence (AI) is becoming. We are witnessing feats performed by machines where we can no longer easily tell the difference between human and computer.
To start, it is the sheer size. GPT-3 has 175 billion trainable parameters in its model. The next closest model has 17B. A training run uses 45 TB of data, almost 10 days of run time and over $10M in compute power. To give you some perspective, the bot you are using on your job posting site probably trains in less than 3 mins. GPT-3 then takes this sophisticated model and huge training dataset and mixes it with the key ingredient: human expertise. In three waves, humans provide feedback to the machine, help it learn, and reinforce good outcomes. Yes, AI is not magic. Humans play a critical role. Version 3 of this model took 3 years of engineering, training, feedback, and revisions.
GPT-3 can be broken down into two primary functions: language understanding and generative AI (composing answers). With the former, GPT-3 excels at interpreting human language and deriving meaning from it. With the latter, it creates its own answers, but those responses can be wildly incorrect, making it a less-than-ideal choice for enterprise use. Additionally, GPT-3 is built on public information that is two years old, so it cannot handle current events, making it especially ill-suited for enterprise use. Let’s compare a Google search to GPT-3.
In a business setting when someone asks about how much vacation time they get, the answer depends on their employment status, their union contract and even the country they reside in. GPT-3 can’t handle that type of personalization. Further, you want your organization’s bot to understand your lingo, your acronyms, and your slang. GPT-3 is one model trained for everyone. Its training data is not your organization’s data. Even though GPT-3 has limits in an enterprise setting, it will no doubt influence the future direction of IT departments.
Because our blog readers are most interested in enterprise software, we must examine the effect GPT-3 will have on the enterprise. To start, it will change user expectations. Let’s take a trip down memory lane….it was 2008 and Steve Jobs had just launched the App Store followed by the famous ad campaign, there’s an app for that! I remember this year well. Most of our customers never spent a moment thinking about mobile or mobile apps. After all, enterprise software was too complicated for that tiny screen!
We can sometimes forget that our enterprise users are consumers, too. They go to movies, buy groceries, go to restaurants, seek entertainment, and so on. Enterprise users, like everyone else, fell in love with mobile apps, came back to work or school with new expectations, and started demanding mobile apps for enterprise functions. Schools and organizations scrambled. Everyone wanted an app. There were RFPs every day calling for proposals from vendors.
Do we think it will be any different this time? ChatGPT (the chatbot built on the GPT-3 model) will work its way into consumer life. Your users will relish the productivity gains it provides. Think about how we find and retrieve information today. We use Google and Google gives us pages and pages of links that all look the same. We have no idea where to click. If we ask that same question to GPT, we get a two-sentence answer that is right on the money. Imagine that instead of pointing, clicking, searching, reading, we just asked and got the answer…
Students and employees are routinely on the search for information; be it about a policy, a form to fill out, a piece of data or analytics. Now imagine a user interface powered by chat AI that gives you an answer quickly, succinctly, and with accuracy that beats human beings! The old way of searching intranets or knowledge base articles is going the way of the dinosaur. (Or should I say punch card?) The flaws of the search result page UI are already being exposed.
GPT will also prove the demand for one-stop shopping: One place to ask any question. While each of these SaaS applications may have some semblance of a bot, they all live on islands without any connection to each other; islands with no bridges. ChatGPT has shown users the value of being able to handle any question you throw at it.
The GPT model, while not perfect, certainly has some capabilities that are pushing the AI universe ahead by enormous leaps. Ida, IntraSee’s very own digital assistant, operates much like ChatGPT, but at a different scale, where we build and train to each client’s uniqueness. Starting with version 23.01 due in April 2023, Ida will introduce its first integration with GPT-3.5. Ida taps into the power of the GPT model to improve upon the secured, personalized answers Ida provides. More to come with the release notes of 23.01.
