AI coding is now everywhere. But not everyone is convinced. Depending who you ask, AI-powered coding is either giving software developers an unprecedented productivity boost or churning out masses of poorly designed code that saps their attention and sets software projects up for serious long term-maintenance problems. The problem is right now, it's not easy to know which is true. As tech giants pour billions into large language models (LLMs), coding has been touted as the technology's killer app. Executives enamored with the potential are pushing engineers to lean into an AI-powered future.

Each year, we spend months researching and discussing which technologies will make the cut for our 10 Breakthrough Technologies list. We try to highlight a mix of items that reflect innovations happening in various fields. We look at consumer technologies, large industrial-scale projects, biomedical advances, changes in computing, climate solutions, the latest in AI, and more. We've been publishing this list every year since 2001 and, frankly, have a great track record of flagging things that are poised to hit a tipping point. It's hard to think of another industry that has as much of a hype machine behind it as tech does, so the real secret of the TR10 is really what we choose to leave off the list.

In the meantime, here are three technologies that we considered including on the 2025 list but ultimately decided to leave off. Though these nominees didn't make the cut this year, they're still worth keeping an eye on. Virtual power plants are energy systems that link together many different technologies to both generate and store power. They allow utility companies to connect solar panels and wind turbines with grid batteries and electric vehicles, and to better manage the flow of power across the grid. During times of peak electricity usage, software linked to smart meters may one day automatically decide to power someone's home by drawing electricity from a fully charged EV sitting in a neighbor's garage, thereby reducing demand on the grid.

Machine learning (ML) models are valuable tools for analyzing the impact of technology using patent citation information. However, existing ML-based methods often struggle to account for the dynamic nature of the technology impact over time and the interdependencies of these impacts across different periods. This study proposes a multi-task learning (MTL) approach to enhance the prediction of technology impact across various time frames by leveraging knowledge sharing and simultaneously monitoring the evolution of technology impact. First, we quantify the technology impacts and identify patterns through citation analysis over distinct time periods. Next, we develop MTL models to predict citation counts using multiple patent indicators over time. Finally, we examine the changes in key input indicators and their patterns over different periods using the SHapley Additive exPlanation method. We also offer guidelines for validating and interpreting the results by employing statistical methods and natural language processing techniques. A case study on battery technologies demonstrates that our approach not only deepens the understanding of 1 technology impact, but also improves prediction accuracy, yielding valuable insights for both academia and industry.

Despite the usefulness of machine learning approaches for the early screening of potential breakthrough technologies, their practicality is often hindered by opaque models. To address this, we propose an interpretable machine learning approach to predicting future citation counts from patent texts using a patent-specific hierarchical attention network (PatentHAN) model. Central to this approach are (1) a patent-specific pre-trained language model, capturing the meanings of technical words in patent claims, (2) a hierarchical network structure, enabling detailed analysis at the claim level, and (3) a claim-wise self-attention mechanism, revealing pivotal claims during the screening process. A case study of 35,376 pharmaceutical patents demonstrates the effectiveness of our approach in early screening of potential breakthrough technologies while ensuring interpretability. Furthermore, we conduct additional analyses using different language models and claim types to examine the robustness of the approach. It is expected that the proposed approach will enhance expert-machine collaboration in identifying breakthrough technologies, providing new insight derived from text mining into technological value.

Why does AI being good at math matter? Last week the AI world was buzzing over a new paper in Nature from Google DeepMind, in which the lab managed to create an AI system that can solve complex geometry problems. This is the second time in recent months that the AI world got all excited about math. The rumor mill went into overdrive last November, when there were reports that the boardroom drama at OpenAI, which saw CEO Sam Altman temporarily ousted, was caused by a new powerful AI breakthrough. It was reported that the AI system in question was called Q* and could solve complex math calculations.

The start of a new year offers a great opportunity to reflect while also thinking about what's to come. That is especially true for us, as this year marks the 125th anniversary of MIT Technology Review. And so it's fitting that we kick off the year with our annual list of 10 Breakthrough Technologies that our reporters and editors think will have the biggest impact on the world in the years to come. We began putting this list together in early summer last year, and have debated over it ever since. Read the full list of our 10 Breakthrough Technologies, and if you're interested in hearing more about what did and didn't make the cut this year, tune into our LinkedIn Live today at 14:30 EST, which you can register for here.

We haven't always been right (RIP, Baxter), but we've often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT). Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn't pick this time--and why we've left them off, for now. Alzmeiher's patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain.

'Our operating model is high-touch," says Gideon Argov, managing partner at New Era. The Israel-US venture capital fund recently announced a $140 million second investment fund and is looking toward a third fund to leverage its latest success. New Era's funds are already valued at more than $500m. Established in 2017 by Argov and Ran Simha, the fund focuses on early-stage investments in Israeli start-ups that use breakthrough technologies, emphasizing artificial intelligence and machine learning."We "We think that Israeli companies should not be sold prematurely. They need to become established outside Israel to realize their full potential and develop a global reach. "It is important that they attain this regardless of whether they become public companies.

Meal prep just got considerably easier thanks to a robotic kitchen assistant that will cook 5,000 different recipes from scratch -- and even do the dishes afterwards. Developed by UK-based Moley Robotics, the cooking automaton has two dextrous hands and was modelled to mimic the 2011 MasterChef winner, Tim Anderson. The'Automated Kitchen' can gather ingredients from a smart fridge, fill pans, mix, pour, adjust hob temperatures and serve up -- just like a regular cook would. Unlike a human, however, it will never complain about having to do the washing up. The Automated Kitchen is the product of a collaboration of around one hundred engineers and designers -- along with three award-winning chefs -- and took six years to be fully realised and market-ready.