Over 5 years ago I started a chatbot (ad)venture called Blits.ai because I believed the way we as humans interact with data will change to a more conversation-based approach. With the current ChatGPT hype, more and more people are coming to the same conclusion.
Over 5 years ago I started a chatbot (ad)venture called Blits.ai because I believed the way we as humans interact with data will change to a more conversation-based approach. With the current ChatGPT hype (generative AI), more and more people are coming to the same conclusion which is great, but we are not clearly there yet.
The way we as humans interact with data will change to a more conversation-based approach - 2017
As someone that has been working with GitHub Co-pilot, and GPT-3 for almost 2 years, including client cases and real-world problems, I would like to share my take on the current state, challenges, and ways it could be beneficial to any business.
You wake up in the morning and try to call your accountant for discussing your next tax income statement. You’re not calling for a typical tax question, because most of them are now already correctly answered by Google or Bing. In addition, your start to rely on the financial advice that is sent to you via your accounting software. You now need specialized advice that requires complete oversight and insights into your personal or business life, which still requires some human expertise. Most accountants in the world would like to spend 80% of their time on financial advice, now this might be within reach for a lucky few, but the rest are in decline or taken over by online competitors.
Next, you call your notary, which has just spent 80% of his time not writing but validating the automatically created investment terms for your new investors. His prices are still way too high, but because you still need his sign-off and ‘expertise’ you’ll allow it. The notary market has shifted from a manual business (.docx) to a digital business (.ai). The transition had a high similarity with the shift from physical stocks to digital stocks on Wall Street (quick and dirty). Almost all of the consultancy-driven business models have a more value-based approach and can’t sell their expertise of public knowledge that well anymore.
While waiting for a train, you read a news article that is clearly written with the help of generative AI. You feel like you’ve already read this article 10 times but it’s highly efficient and gets the story well told. Most new content is in this format, but it’s still in some way audited by humans. AI Auditing tools and human validation becomes popular and you start to value creative content more and more, you might even get a subscription to a newspaper that has a slogan like '100% human.
The major tech companies have their daily troubles with anti-trust laws and privacy. Not due to the rise of services like ChatGPT, but due to the fact the incentive to create new original blogs or websites is in decline. 80% of the questions are now directly answered by models like ChatGPT. This results in the fact that 80% less traffic is going to the actual website that created the original content anymore, lowering the need for new content.
We predict therefore that the frequently occurring questions and assignments can be performed much faster and better by generative AI models like ChatGPT, but the customization of your business still requires the opposite.
When companies started the implementation of chatbot technology with the use of intent recognition (the smart part of a chatbot), companies quickly found out it’s a lot of work to shape every dialog and build natural language models based on data that company context. The upside is that a company can shape the customer journey from a to z with much detail and precision, something most companies need for their clients or customers.
In Chatbot technology it takes a lot of effort and money to make make a chatbot conversation feel like a human one.
ChatGPT works the other way around, it’s extremely easy to feel like a human conversation. The downside, however, is that it’s not aware of you specific context of your use cases and company specifics. Next to that it only has access to publicly available internet data until 2021 and the model must be heavily regulated by humans at OpenAI on abuse to make sure it doesn’t end up racist or promoting your competitor.
With chatGPT it takes a lot of effort to shape the conversation towards your company context, data and policies.
There are some methods to feed GPT-3 with your company context like GPT-Index but it is very limited and the result is basically a black box for what it will answer, with a probability of it being wrong. This doesn’t mean that it’s useless, but I don’t expect it will be a Google killer soon, as most people still value reliability over convenience.
As these large language models are improving over time the quality is increasing every day. The more these AI models are used the better they get. A lot of industries are now at the opportunity to start experimenting with technology to improve their business. Let me give some simple examples you can start today.
We are currently actively helping companies make the most use of this technology and are in search of major consultancy-based companies that want to be a disruptor in this market.
In my previous article about AI, I mentioned how everyone suddenly seems to be an AI expert and how that creates a lot of noise. In this post, I'll break down one of the most important topics: how (and why) you should add your company's data to large language models.
Let's be honest: I think it's great that technology is so embedded in our daily lives. It helps us get knowledge faster, complete tasks more efficiently, gives us inspiration, and occasionally scares the hell out of us. But after spending a ridiculous amount of time with these technologies, I feel it's time to reflect on what I really hate about AI.
Someone once told me, stupid questions don't exist. This might be true, but people are still afraid to look stupid by asking questions that are assumed to be general knowledge. In this article, my goal is to get everyone up-to-speed on the basics of AI.
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