Top 5 AI Predictions From Experts In 2025
While no one has a crystal ball to predict the future — the future of AI in 2025 looks bright.
The past 12 months have seen major advances across AI. These include everything from the buildout of massive supercomputers for ongoing AI training to large language models learning how to use computers as well as AI’s expansion into internet search and real-time political polling details.
For this piece of prognostication perspective, five experts were asked to independently share their top three AI projections. Based on the insights gleaned from this handful of AI experts, 2025 looks to be even more revolutionary for the sector than 2024 with some surprising predictions and trends likely to develop through the end of the new year.
AI Agents Come Of Age
Virtually all the experts mentioned the rise of AI Agents — computer programs that can think, learn and act autonomously — as the most significant trend to watch for in the coming year.
Pascal Bornet is an award-winning expert, keynote speaker and author — his latest book, IRREPLACEABLE: The Art of Standing Out in the Age of Artificial Intelligence, distills his research into practical strategies. He says 2025 will push the AI boundaries beyond our expectations.
“Agentic AI will redefine autonomy. These systems won’t just respond — they’ll take initiative, anticipate needs and act independently, solving problems before we even notice them, becoming indispensable collaborators in both work and life,” Bornet shared in an email.
More on top 5 AI predictions for 2025 on Forbes
Gen AI For Good With James Hodson: Using AI To Create A Better Tomorrow
What if instead of focusing on productivity, we used AI technology to solve our most pressing, human problems like where to find medicine or how to avoid the collapse of the banking system in Ukraine? Listen to James Hodson, Co-Founder and CEO of AI for Good, talk about how AI technology can positively transform communities, but only if we provide the necessary infrastructure and education to get people on board.
I Put Gemini Vs ChatGPT To The Test With 7 Prompts — Here's The Winner
Google’s Gemini and OpenAI’s ChatGPT are the most widely used artificial intelligence platforms today. Each of them has millions of active users and regularly gets new features. In December alone Google and OpenAI both dropped improved image generation models, AI reasoning, and research tools to make finding information easier.
Both have a voice assistant in the form of Advanced Voice from OpenAI and Gemini Live from Google and both allow you to connect to external data sources and build projects.
Human evaluation tests see the leading models from both Google and OpenAI regularly swap places in chatbot arenas and our own comparisons have resulted in Gemini winning sometimes, ChatGPT other times.
To find out the winner, after 12 Days of OpenAI announcements and a December of Google Gemini drops, I’ve devised 7 prompts to put them to the test.
Creating the prompts
For the test, I’m using ChatGPT Plus and Gemini Advanced so I can make use of the best models both platforms have to offer. The subscription version is about the same price for both — around $20 per month, so that is also a good comparison point.
I’m testing image generation and analysis, how well they create the code for a game and creative writing skills. Then I’ve also come up with prompts to put each bot's research models to the test — ChatGPT’s o1 and Gemini’s 1.5 Deep Research.
More on the battle of the AI giants on Tom’s Guide
Ranking 13 Of The Most Popular AI Video Tools (Q4 2024 Tier List) | The Next Wave
How are AI video tools revolutionizing content creation? Matt Wolfe and Nathan Lands are joined by Tim Simmons, founder of Theoretically Media, to delve into the latest advancements in AI video tools.
This episode ranks 13 of the most popular AI video tools of Q4 2024, discussing their features, strengths, and weaknesses.
They explore the capabilities of tools like Sora, Runway, and Adobe Firefly, and predict future developments in AI integration. The conversation highlights the evolving landscape of AI video generation and its impact on content creation.
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Robots Are Learning To Conduct Surgery On Their Own — By Watching Videos
The artificial intelligence boom is already starting to creep into the medical field through the form of AI-based visit summaries and analysis of patient conditions. Now, new research demonstrates how AI training techniques similar to those used for ChatGPT could be used to train surgical robots to operate on their own.
Researchers from Johns Hopkins University and Stanford University built a training model using video recordings of human-controlled robotic arms performing surgical tasks. By learning to imitate actions on a video, the researchers believe they can reduce the need to program each individual movement required for a procedure. From the Washington Post:
“The robots learned to manipulate needles, tie knots and suture wounds on their own. Moreover, the trained robots went beyond mere imitation, correcting their own slip-ups without being told ― for example, picking up a dropped needle. Scientists have already begun the next stage of work: combining all of the different skills in full surgeries performed on animal cadavers.”
To be sure, robotics have been used in the surgery room for years now—back in 2018, the “surgery on a grape” meme highlighted how robotic arms can assist with surgeries by providing a heightened level of precision.
Approximately 876,000 robot-assisted surgeries were conducted in 2020. Robotic instruments can reach places and perform tasks in the body where a surgeon’s hand will never fit, and they do not suffer from tremors. Slim, precise instruments can spare nerve damage. But robotics are typically guided manually by a surgeon with a controller. The surgeon is always in charge.
More on Johns Hopkins and Stanford’s robotic AI surgeon on Gizmodo
CNBC | AI Trends To Watch in 2025
CNBC's Deirdre Bosa reports on what to expect from the AI race in 2025.
Coming Year | GenAI Enters The Roll-Up-Our-Sleeves-And-Get-To-Work Phase
Anecdotally, it’s being said, at least anecdotally, that the AI hype has been cooling as of late. Gartner, the consultancy that devised its oft-quoted hype cycle, agrees.
“GenAI has passed the Peak of Inflated Expectations,” wrote Afraz Jaffri, analyst with Gartner, in a bulletin published in November. “By the end of 2024, value will be largely derived from projects based on familiar AI techniques, either stand-alone or in combination with GenAI, that have standardized processes to aid implementation.” In other words, time to get past the shiny-new-thing stage to roll up one’s sleeves and make it all work.
Industry leaders agree that it’s time for the hard and purposeful work to begin. “While there is a lot of hype around AI, we have seen impressive outcomes in such a short span of time,” said Sri Elaporu, global head of the AWS Generative AI Innovation Center. “Companies are already seeing real benefits of generative AI.”
"There is certainly a lot of hype and potentially unrealistic expectations around AI, especially generative AI, in the short run," said Steve Chase, vice chair of AI and digital innovation at KPMG. "This can put unrealistic pressure on teams to deliver immediate results and cause doubts about continuing investments when those lofty expectations are not met right away. However, many companies are already seeing tangible benefits from AI. The mid to long-term potential of AI is likely understated."
More on GenAI rolling up it’s sleeves in 2025 on Forbes
Yann LeCun: AI Doomsday Fears Are Overblown | Into The Impossible Podcast
What are the current limitations of AI? What advancements do we need to achieve human-like intelligence? And how can we develop AI safely to align with our values?
Here today to offer us an astounding look behind the scenes of AI development is Meta’s chief AI scientist, Yann LeCun! Yann is a pioneer in AI and a Turing Award winner who has been at the forefront of major breakthroughs in machine learning and neural networks.
As the architect behind transformative AI technologies, Yann joins us to demystify the path toward human-level intelligence and the challenges that lie ahead. He also introduces the Joint Embedding Predictive Architecture (JEPA), a potential game-changer for enabling AI to model and predict complex real-world scenarios.
Thats all for today, however new advancements, investments, and partnerships are happening as you read this. AI is moving fast, subscribe today. Happy New Year!