Chatbots will change the way we shop
Imagine a world where you have a personal shopper at your disposal 24 hours a day, 7 days a week—an expert who can instantly recommend a gift to even the hardest-to-find friend or relative, or scour the Internet to compile a list of the best bookcases available within your tight budget. What's more, they can analyze the strengths and weaknesses of a kitchen appliance, compare it to a seemingly identical competitor, and find you the best deal. Then, once you are happy with their offer, they will also take care of the purchasing and delivery details.
But this extremely knowledgeable customer is not a knowledgeable person at all—it's a chatbot. This is also not a distant forecast. Salesforce recently said AI is expected to drive $263 billion in online shopping this holiday season. This is about 21% of all orders. Experts are betting that AI-powered shopping will become an even bigger business in the next few years. It is estimated that agency trading will generate between $3 trillion and $5 trillion annually by 2030. research from the consulting company McKinsey.
It's no surprise that AI companies are already investing heavily in making shopping through their platforms as easy as possible. Google Gemini app Now you can use the powerful capabilities of the company Shopping schedule a set of product and seller data and can even use its agent technology to call stores on your behalf. Meanwhile, back in November, OpenAI announced ChatGPT shopping feature is capable of quickly generating shopping guides, and the company has entered into agreements with Walmart, Target and Etsy to allow shoppers to purchase products directly as part of a chatbot interaction.
Expect more deals like this to be struck next year as the time consumers spend interacting with AI continues to rise and web traffic from search engines and social media continues to plummet.
—Rhiannon Williams
LLM will make an important new discovery
I'm going to play it safe here, right out of the gate. It's no secret that large language models spew out a lot of nonsense. Unless you get lucky with monkeys and typewriters, graduate students won't discover anything on their own. But LLM still has the potential to expand the boundaries of human knowledge.
We got a glimpse of how this could work in May when Google DeepMind unveiled AlphaEvolve, a system that used Gemini's proprietary LLM program to come up with new algorithms for solving unsolved problems. The breakthrough was combining Gemini with an evolutionary algorithm that checked its proposals, selected the best ones and fed them back to the LLM to make them even better.
Google DeepMind used AlphaEvolve to find more efficient ways to manage power consumption in Google's data centers and TPU chips. These discoveries are significant, but not game-changing. More. Researchers at Google DeepMind are now pushing their approach to see how far it will go.
And others hastened to follow their example. A week after the release of AlphaEvolve, Asankhaya Sharma, an artificial intelligence engineer in Singapore, shared OpenEvolve, an open-source version of Google's DeepMind tool. In September, Japanese firm Sakana AI released a version of the software called SinkaEvolve. And in November, a team of American and Chinese researchers unveiled AlphaResearch, which they claim improves on one of AlphaEvolve's mathematical solutions that already outperforms human ones.
