Chatbots will change the way we shop
Imagine a world in which you have a personal shopper available to you around the clock – an expert who can instantly recommend a gift for even the most difficult friend or relative, or scour the web to compile a list of the best bookcases available within your limited budget. Even better, they can analyze the strengths and weaknesses of a kitchen appliance, compare it to its seemingly similar competitors, and find the best deal for you. Then once you are satisfied with their suggestion, they will also take care of the purchasing and delivery details.
But this hyper-savvy buyer isn’t a human with no clue at all—it’s a chatbot. This is not even a distant prediction. salesforce recently Said It is estimated that AI will contribute $263 billion to online shopping this holiday season. This is approximately 21% of all orders. And experts are betting on AI-augmented shopping becoming even bigger business in the next few years. According to this, by 2030, $3 trillion to $5 trillion will be earned annually from agentive commerce. Research From consulting firm McKinsey.
Not surprisingly, AI companies are already investing heavily in making shopping through their platforms as seamless as possible. Google’s gemini app Now you can tap into the company’s strengths shopping graph data set of products and sellers, and can also use its agentive technology to call stores on your behalf. Meanwhile, in November, OpenAI announced a ChatGPT Shopping Facility Able to compile buyer guides faster, and the company has made deals with Walmart, Target, and Etsy to allow buyers to purchase products directly within chatbot interactions.
Expect more deals like this to happen within the next year as consumers’ time spent chatting with AI increases and web traffic from search engines and social media continues to decline.
,rhiannon williams
LLM will make an important new discovery
I’m going to defend here, right out of the gate. It’s no secret that big language models spit out a lot of nonsense. Unless monkeys and typewriters have some luck, LLM won’t be able to discover anything on its own. But LLM still has the potential to expand the range of human knowledge.
We got a glimpse of how this might work in May, when Google DeepMind revealed AlphaEvolve, a system that used the firm’s Gemini LLMs to come up with new algorithms to solve unsolved problems. The breakthrough was to pair Gemini with an evolutionary algorithm that examined its suggestions, picked the best suggestions and fed them back into LLM to make them even better.
Google DeepMind used AlphaEvolve to come up with more efficient ways to manage power consumption by data centers and Google’s TPU chips. Those discoveries are important but not game-changing. As yet. Google DeepMind researchers are now pushing their approach to see how far it will go.
And others have been quick to follow his lead. A week after AlphaEvolve’s arrival, Anyarya Sharma, an AI engineer in Singapore, shared OpenEvolve, an open-source version of Google DeepMind’s tool. In September, Japanese firm Sakana AI released a version of the software called SyncEvolve. And in November, a team of American and Chinese researchers unveiled AlphaSearch, which they claim improves on one of Alphavolve’s already better-than-human math solutions.
