Chatbots will change the way we shop
Imagine a world in which you have a personal shopper 24-7—an expert who can instantly recommend a gift for even the most difficult-to-shopper friend or relative, or trawl the web to compile a list of the best books available on your tight budget. Better yet, they can analyze the strengths and weaknesses of a kitchen appliance, compare it to seemingly similar competition, and find you the best deal. Then once you are happy with their advice, they will take care of the purchase and delivery details as well.
But this super-savvy shopper isn’t a human at all—it’s a chatbot. This is also not a distant prediction. Salesforce recently said It expects AI to drive $3.263 billion in online purchases this holiday season. This is 21% of all orders. And experts are betting on e-commerce shopping becoming an even bigger business in the next few years. By 2030, between $3 trillion and $5 trillion will be generated annually from agent commerce. Research From the consulting firm McKinsey.
Not surprisingly, AI companies are already investing as much in making purchases through their platforms as possible. Google’s The Gemini app Now tap into the company’s powerful Shopping graph A data set of products and sellers, and can even use its agent technology to call stores on your behalf. Meanwhile, back in November, OpenAI announced CHAT GPT Shopping Feature Able to quickly set up buyer leads, and the company has agreements with Walmart, Target, and Etsy to allow shoppers to buy products directly in chatbot interactions.
Expect many more such deals to be struck within the next year as consumers continue to spend time chatting with AI, and web traffic from search engines and social media continues to decline.
b (b (Rhiannon Williams
An LLM will make an important new discovery
I’m going to hedge right out of the gate here. It’s no secret that big language models make a lot of nonsense. Unless it is with the luck of monkeys and typewriters, LLMS itself will discover nothing. But LLM still has the potential to expand the boundaries of human knowledge.
We got a glimpse of how this might work in May, when Google DeepMind revealed to Alpha Alphavolo, which used the firm’s Gemini LLM to come up with new algorithms to solve unsolved problems. The breakthrough involved pairing Gemini with an evolutionary algorithm that tested its suggestions, picked the best ones, and fed them back into the LLM to make them even better.
Google DeepMind used Alpha Alphavolo with more efficient ways to manage power consumption by data centers and Google’s TPU chips. Those discoveries are important but not game-changing. Still. Google DeepMind researchers are now pushing their approach to see how far it will go.
And others have been quick to follow their lead. A week after Alphavolo came out, Asankhaya Sharma, an AI engineer in Singapore, shared an open-source version of Google’s DeepMind tool on Openvolo. In September, Japanese firm Sakana AI released a version of the software called Sunkivolo. And in November, a team of American and Chinese researchers unveiled AlphaSerch, which they claim is one of the best human math solutions to the alphanumeric alphabet.