My thoughts on AI in 2023
OpenAI took the world by storm in 2023 with the relase of ChatGPT. Other companies quickly followed suit, introducing their own competitors to ChatGPT. In this update, I want to write down my thoughts on some of the biggest players in the field of generative AI.
When I use the term “generative AI,” I am referring to a computer program that can generate text, images, or other types of content. These programs can understand natural language and follow complex instructions. One notable example of a generative AI is OpenAI’s “GPT-4,” commonly known as ChatGPT. Another well-known generative AI is Midjourney, which specializes in generating images from text descriptions. In this update, I will focus on AI with text output, as that is the area I have the most experience with.
ChatGPT
ChatGPT was the breakthrough technology that thrust generative AI into the spotlight. When it launched last year, its state-of-the-art language model wowed the world with its human-like responses and ability to interpret plain language. It became particularly popular among programmers for its ability to turn vague instructions into functional code.
However, its capabilities alone didn’t make ChatGPT revolutionary. OpenAI made it incredibly user-friendly by packaging the technology as a chatbot, making it intuitive to even the least technical users. As a result, 2023 became the year of OpenAI and ChatGPT.
Throughout the past year, ChatGPT has maintained its edge over competitors, continually improving to understand images and speech. OpenAI even released a voice assistant that outperformed Siri, Alexa, and Google’s equivalent. Despite the significant efforts from larger companies like Amazon and Google, they’re still catching up. For anyone looking to use a generative AI product, ChatGPT remains hard to beat.
Nevertheless, I have concerns about OpenAI’s future. Transitioning from an AI safety research group to a profit-seeking enterprise presents challenges. Earlier this year, I predicted OpenAI would focus on direct-to-consumer products and stop selling APIs, but I was wrong. They’ve continued selling APIs, a strategy that risks making their business easy to replace. OpenAI has proven adept at releasing innovative products, but can they sustain this reputation? Failure to develop a competitive moat might result in losing their window of opportunity, potentially leading to an exodus of their top talent.
ChatGPT continued to stay ahead of the competition during the past year. It remains the most practical AI and OpenAI further improved it with the ability to understand images and speech. Recently, OpenAI released a voice assistant that blew Siri, Alexa, and (whatever Google calls their product) out of the water. Even now, much larger companies like Amazon and Google are still playing catchup. If you want to use a generative AI product, it is hard to go wrong with ChatGPT.
Perplexity
Over the past year, Perplexity was the generative AI product I used the most. Perplexity uses the same technology behind large language models (LLM) to build a better search engine. It can be very costly to re-train a LLM on new contents from the web. Technologists uses a technique called retrieval augmented generation (RAG) to teach a LLM with little cost. In the RAG technique, new information is transformed into a numerical format (an embedding), this numerical format is easy to store in a database and process. When a question is posed to Perplexity, it uses the same algorithm to chnage the question into an embedding also. Then Perplexity can compare the question embedding to the database and look up relevant information. It’s like teaching a LLM to use an encyclepedia. The LLM doesn’t need to memorize the encyclepedia, it can supplement what’s “in the LLM’s head” with knowledge in the encyclepedia.
Both Bing and Google are using generative AI to enhance or replace traditional search. But Perplexity has been the best product in this area. Perplexity is the product I use If want information backed by reliable sources and I want to ask follow-up questions. Under the hood, Perplexity uses ChatGPT and other top-of-the-line models and they use Bing as their encyclepedia.
I am also concerned about the long term viability of Perplexity. It’s a great product, but it is competing against Google on Google’s turf. Assuming Google can get their act together and incorporate RAG into Google search, why would anyone switch to Perplexity? Even assuming it displaces Google as the internet’s search engine, how will they monetize? Using LLMs to post-process search results means the LLM will also strip out paid ads. Currently, Perplexity tries to make money though a subscription model. Is that really sustainable?
Google Bard
Google Bard was seen as a piece of junk when it was first released. Most responses would come back with “I am a large language model. I cannot perform that task.” Thankfully, it is much better now. Like ChatGPT, it understands images as well as text. Unlike ChatGPT, we hear little about its improvements and Google’s plans for it. Google Bard really seems to struggle with the “Gogogle has too much bureaucracy to polish a product” symptom. Recently, Google claimed that they improved Bard with a LLM called Gemini which rivals GPT-4. It can also now acccess your documents in Google drive, which is a huge win for companies using the Google Workplace suite of products. I found the drive integration to be very useful. With so much data in their cloud, Google Bard has a lot of potential.
The question remains: Will Google Bard will linger in limbo like other Gogole products? Recently I got access to yet another Google product called Notebook LM. Much like Bard, it uses generative AI to help you understand and make use of documents in your Google drive. Which product is Google going to really invest in? Who knows? If Google’s track record with messaging stands, their incredible advantage in AI might just go to waste.
Github Copilot
Of all the generative AI offerings, Copilot was the least impressive product. Github Copilot featured the abiility to generate the implementation of a function by its name alone. That’s fine, I suppose. But I rarely want AI to write code for me. I prefer to write the code myself, then have AI review and improve it. I didn’t find Github Copilot useful when I tried it. I think the future in AI code assistants will take the form of automated code reviews and refactoring tools. The current completion-based Copilot will eventually be replaced by more workflows.
We will see more generative AI products in 2024. I believe there will be significant investments in the retrieval-augmented generation space. In the workplace, I can imagine a chatbot replacing “that guy who’s been around forever and knows all the answers.” I can also see more uses of products like Notebook LM to help with research and personal development. In the hardware space, I can see augmented reality goggles which uses AI to help you navigate the world. These opportunities promise an exciting year ahead.