A newly released list of the topmost AI research areas is worthy of rapt attention.gettyIn todays column, I share and mindfully explore a newly released official listing of the topmost AI topics.This particular listing is important since it was produced by an esteemed AI academic and professional association and garners a lot of hefty weight in the AI community at large. The group that put this together is the non-profit Association for the Advancement of Artificial Intelligence (AAAI). They have published a carefully composed list that turns out to contain seventeen of what are considered the highest-priority research areas of our times.To bring you up to speed on this, I will briefly identify and explain each of the seventeen AI topics so that youll know what they are and why you should care about each of them.Lets talk about it.This analysis of innovative AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).Sidenote: In case you arent familiar with the AAAI, please know that it is well regarded in the AI community. Im honored to say that Ive been a speaker at AAAI conferences, served on the editorial board for their flagship publication, and participated in various workshops and other activities. It is a great organization and one that anyone keenly interested in the especially technological and computational depths of AI ought to consider looking into.Why AI Research Is ImportantI suppose that I dont have to expend much energy here telling you that AI research is crucial to the future of AI.This seems axiomatic.You see, many if not most of the major AI breakthroughs have arisen from AI research. It typically goes like this. An AI lab opts to toy with this or that new fangled idea or theory, and voila, something of grand interest occurs. Some of the lab members form a startup and turn the AI prototype into a workable and useable AI system. Or a big company comes along and licenses the AI from the lab. Etc.All manners of AI hot products began as fledgling AI research projects.Nowadays, the AI labs of universities and colleges are often eclipsed by the professional AI labs at high-tech firms. Tech firms put big bucks into their AI labs. There is usually intense pressure to produce something that will warrant those investments. Some assert that this limits what those bang-for-the-buck labs are willing to play around with, whereas academic labs are presumably freer to make wilder speculative efforts.See my recent coverage on the ongoing ins and outs of academic AI foundational research versus industry-based AI research at the link here.What AI Research Is WorthySuppose that you are someone trying to figure out what area of AI research is worth pursuing.For example, graduate students majoring in AI have a mighty stake in which topics are hot and which are not. They indubitably want to pick a topic that has legs and will put them at the forefront of AI. If they choose an AI topic that seems to be a dead-end or on the backburner, they might accidentally waste their time and attention during the throes of producing their thesis or dissertation. They could graduate with expertise in something few want or care about. Sad face.Professors are pretty much in a somewhat similar boat. A faculty member that aligns with a considered out-of-date AI research area is bound to have a rough time of things. Few graduate students will gravitate to them. Writing journal articles will be tough since the topic theyve chosen is no longer in favor. And on it goes.Practitioners also have a vested interest in hot AI research topics. Imagine you are working for a company as an AI developer, and they put you onto a dogeared AI project. The AI in this instance is past its prime. Your value in the marketplace is going to decline with each passing day. Not good.Venture capital firms and investors in AI are supremely interested in the hotness of AI research areas.Why?They want to get in on the ground floor of burgeoning AI, finding the next big thing that will strike oil, as it were. You see, you can put pennies into such AI, and when it later takes the world by storm, wham, your small investment blossoms into a heralded fortune. Plus, you look as smart as a whip for having the vision and fortitude to find the gem that no one else saw.Plenty of other stakeholders care about AI research topics.Just to give you another final example for now, think about lawmakers, policymakers, regulators, politicians, and the like. Some want to curtail AI before it gets out of hand; ergo, they want to know early on what is coming out of the AI research labs. Others hope that AI research is going to provide tremendous benefits, and they want to grease the skids to ensure that the AI becomes real-world-ready as soon as possible. For my coverage on the legal and ethical aspects of the latest in AI, see the link here, and for the contrast between AI doomers and AI accelerationists, see the link here.A List Of Prime Purpose And ValueI trust that you can plainly see that a list of topmost AI research topics is potentially worth its weight in gold.Be aware that there are plenty of such lists floating around. You can find them via the simplest of Internet searches. The thing is, not all lists are of equal value. Some lists are more worthy than others. Watch out for out-of-date lists. Remain vigilant to discern if a given list is unduly tilted by advertising or via the use of sponsors.Important questions to ask include but are not limited to:Who made the list?Why did they do so?When did they do so?What does the listing contain?How did they put together the list?Does the list hold up under close scrutiny?Put a mindful grilling to any of the many such lists before opting to run with the contents of the list.That being said, lets look at a bona fide list that the AAAI recently released in March 2025. Id like to do some unpacking for you about the list. The listing is encompassed in a report entitled AAAI 2025 Presidential Panel On The Future Of AI Research by the Association for the Advancement of Artificial Intelligence (AAAI), published March 2025, and makes these salient overall remarks (excerpts):As AI capabilities evolve rapidly, AI research is also undergoing a fast and significant transformation along many dimensions, including its topics, its methods, the research community, and the working environment.Topics such as AI reasoning and agentic AI have been studied for decades but now have an expanded scope in light of current AI capabilities and limitations.In this overwhelming multi-dimensional and very dynamic scenario, it is important to be able to clearly identify the trajectory of AI research in a structured way.This study aims to do this by including 17 topics related to AI research, covering most of the transformations mentioned above.Notably, they ended up with 17 mainstay AI research topics.You might be wondering why the number isnt a rounded count, such as 15 or 20. Well, you could combine some of the ones theyve listed and bring the count to 15, or you could divide up some of them and stretch the listing to 20.Lets not quibble about it.The Magical Sweet Seventeen Im sure you are busting at the seams and exceedingly eager to see the list. I will list the seventeen and then, after doing so, will walk through each one of them briefly.Also, please know that the AAAI report depicts each topical area, providing a narrative on each key theme, the main takeaways for that topic, the context and history, the current state and trends, the research challenges associated with the topic, the AI communitys opinion about the topic, and cited references. To arrive at the list, the researchers consulted with over two dozen AI experts, surveyed the AAAI community, and held various workshops.Without further ado, heres the list of the topmost AI research topics:(1) AI Reasoning(2) AI Factuality & Trustworthiness(3) AI Agents(4) AI Evaluation(5) AI Ethics & Safety(6) Embodied AI(7) AI & Cognitive Science(8) Hardware & AI(9) AI for Social Good(10) AI & Sustainability(11) AI for Scientific Discovery(12) Artificial General Intelligence (AGI)(13) AI Perception vs. Reality(14) Diversity of AI Research Approaches(15) Research Beyond the AI Research Community(16) Role of Academia(17) Geopolitical Aspects & Implications of AIIf you dont know about some or many of those topics, no worries.You are in the same boat as most people. Few pay attention to more than one or two of those AI topics. Even steeped AI specialists are often focused on just a handful of those topics. They vaguely know of the other subfields. They tend to be narrowly concentrating on a topic that is near and dear to their interests.I would urge anyone seriously considering themselves to be well-versed in AI to at least bone up on all the stated topics.Doing so is a valuable use of your time. The reason that this is the case is that all those AI research topics invariably intermix with the others. It is like a giant puzzle. You ought to know what all the pieces are. You dont have to be an expert in each piece, but you should be familiar with all the pieces and thus be able to grasp the big picture in a cognizant way.For the remainder of this discussion, I will list each topic, provide a short quote from the report that summarizes the topic, and then offer my own thoughts on the topic. Turns out that my column has covered all these topics since I aim to provide both breadth and depth in my analyses -- I will provide a link for each topic in case youd like to learn more about my in-depth commentary on that given topic.Explaining The SeventeenYou can jump to any of the topics that especially interest you or read the entire list. If you think you are already versed in all the topics, skip to the concluding remarks. Its up to you, and I hope youll find my explanations insightful.Lets get underway.(1) AI Reasoning: The ability to reason has been a salient characteristic of human intelligence, and there is a critical need for verifiable reasoning in AI systems.When generative AI first became a public showcase, the way it worked was principally by predicting the next word in a sequence, akin to the autocomplete function in word processing. If you stretch that approach, you could think of responding to a question as a form of autocomplete, whereby the answer or response is about predicting what words come next.Currently, the leading edge of such AI incorporates some form of logic-based reasoning. It isnt solely about word prediction per se. You might make use of steps of logic. Intriguingly, this is reminiscent of the days of expert systems and knowledge-based systems, which are now often considered passe. But the resurgence of symbolic AI, combined with sub-symbolic AI, appears to be at the forefront and leading us to neuro-symbolic or hybrid AI.Exciting times are ahead.For my detailed coverage on AI reasoning, see the link here.(2) AI Factuality & Trustworthiness: Improving factuality and trustworthiness of AI systems is the single largest topic of AI research today, and while significant progress has been made, most scientists are pessimistic that the problems will be solved in the near future.Youve probably heard about AI hallucinations. Though I disfavor the catchphrase because it tends to anthropomorphize AI, nonetheless, it is indeed catchy and we are stuck with the moniker. The issue is that generative AI is working based on probabilities and statistics, said to be non-deterministic, and sometimes will produce a generated response that is a falsehood and not based on grounded facts. This is worrisome, especially if the AI is being used for serious purposes. Trying to resolve the factuality of generative AI is a huge problem, and some believe it is essentially unresolvable, while others disagree.The topic of AI trustworthiness is another blockbuster issue. We are gradually embedding AI into all types of systems, such as AI that runs factories, AI that drives cars, and the like. How can you be sure that the AI will perform correctly? Should you trust AI? What can be done to enhance trustworthiness?For my detailed coverage on AI hallucinations, see the link here, and for AI trustworthiness, see the link here.(3) AI Agents: Agents and multi-agent systems (MAS) have evolved from autonomous problem-solving entities to integrating generative AI and LLMs, ultimately leading to cooperative AI frameworks that enhance adaptability, scalability, and collaboration.You would almost need to be living in a cave that has no Internet connection to not have heard about the latest hot trend involving agentic AI. It goes like this. Conventional generative AI tends to leave you in a lurch by not doing follow-ups. For example, you are planning a vacation, and the AI tells you which flights might be good for you and which hotels to stay at. Unfortunately, the AI stops at that juncture. You must proceed to book the flights and the hotel stays.Instead, the latest approach consists of agentic AI. These AI agents are typically composed of one or more generative AIs that work in concert to accomplish a task from end to end. An agentic AI might help plan your trip and proceed to make all your bookings for you. Nice. Of course, this also increases various concerns, such as what if the agentic AI goes wild and commits you to flights you didnt want and books you into hotels you would never want to set foot in.For my detailed coverage on the latest in agentic AI, see the link here.(4) AI Evaluation: AI evaluation is the process of assessing the performance, reliability, and safety of AI systems.Somebody tells you they have the greatest new AI. It shines and dines. Whatever you want to have done, by gosh, the AI can do it for you. How can you assess or evaluate the AI to see if those brash claims are valid?Thats the importance of AI evaluation as a topic area.We need to keep AI makers and others honest about their outstretched claims. It is easy to make boastful assertions. Partly, they get away with it because the techniques and approaches to AI evaluation are still being figured out for modern-era AI. This is one of those topics that regrettably doesnt get as much attention as it deserves, not being seen as whizbang by some, but AI evaluation is going to rein in the insidious tomfoolery that can mislead and confound society about what AI is and isnt able to accomplish.For my detailed coverage on the latest in AI evaluation, see the link here.(5) AI Ethics & Safety: The ethical and safety challenges of AI demand a unified approach, as both near-term and long-term risks are becoming increasingly interconnected.Would you use a toaster that wasnt cleared as safe? Probably not. You rely upon various standards and testing to inform you whether a given toaster is going to work suitably. The same ought to be the case for AI. AI safety researchers are bent on identifying smart ways to gauge the safety of AI.This takes us into the AI ethics realm, too, and the legal side of AI as well. If an AI maker produces AI that is unsafe, should the AI maker be held responsible? You might say, yes, of course. Suppose, though, that the AI maker handed the AI to someone else who fielded the AI. Who has responsibility now? They might finger-point at each other, trying to shift blame. Ethical and legal dynamics can be complex.For my detailed coverage of AI safety, see the link here, and for my latest on AI ethics, see the link here.(6) Embodied AI: Embodied AI creates intelligent agents that perceive, understand, and interact with the physical world.Embodied AI consists of AI that connects with and can, to some degree, impact the real world via physical actions. Allow me to explain. When you use conventional generative AI, it spits out words. The AI doesnt take any overt actions. It just gives you words. You then potentially act upon seeing those words.The realm of what some refer to as Physical AI is heating up because more and more AI, such as generative AI is being linked with robots and other devices that operate in the physical world. That means that if the generative AI tells a robotic arm to swing this way or that way, someone could potentially get hit. Words are turning into actions.For my detailed coverage about embodied AI, see the link here.(7) AI & Cognitive Science: AI has much to learn from other areas in cognitive science and can in turn contribute much to them.The field of AI and the field of cognitive science are close partners.First, contemplate the realm of cognitive science. How do humans think? Can we learn from the thinking processes of humans to make better AI? Likewise, maybe we can devise AI that gives us insights into how humans think. Perhaps we can simulate human thinking, which in turn will provide new insights into human cognition. They are two peas in a pod.For my detailed coverage on the intertwining role of AI and cognitive science, see the link here.(8) Hardware & AI: Hardware/software architecture co-design for artificial intelligence involves creating hardware and software components that are specifically designed to work together efficiently, maximizing the performance and energy efficiency of AI systems.Billions upon billions of dollars are being spent on building massive data centers that house zillions of high-speed computer servers. So far, it seems like we need the speed of large-scale computing to make further advances in AI. The processing also aids the increasing appetite of hundreds of millions of users who are tapping into generative AI.Aha, what if we could redesign the hardware so that it runs more efficiently and sustainably? That would be good. What if we could redesign AI such that it can run on lesser hardware, such as the advent of small language models (SLMs)? Hardware and software must be devised and utilized in harmony to get where we need to go.For my detailed coverage on AI and advances in hardware, see the link here.(9) AI for Social Good: AI for social good is a subdiscipline of AI research where measurable societal impact, particularly for vulnerable and under-resourced groups, is a primary objective, focusing on areas that have historically lacked sufficient AI research and development.Not everyone is necessarily benefiting from the rapid advances in AI. Bringing together AI ethics and AI law, the realm of AI for social good seeks to bring everyone under the AI umbrella. This is often seen as less vital by those on the AI techie side of things. Thats disconcerting. It takes a village to properly advance AI.For my detailed coverage on AI for social good, see the link here.(10) AI & Sustainability: AI is rapidly transforming industries and holds immense potential to drive sustainability progress, ranging from accelerating the net-zero energy transition to enhancing climate resilience. However, its deployment also raises challenges, such as increasing energy and water demands. Ensuring AI advances sustainability rather than exacerbating environmental risks will require proactive efforts to shape its development, operations, and applications.People dont often think about how much energy it takes to run their queries in generative AI.Those massive data centers are chewing up tons of electricity. The data centers that are water-cooled consume an incredible amount of water. What can be done to ensure that AI is a sustainable resource? Can those devising AI do so in a means that will reduce the footprint of the AI? The same goes for those who field AI and those who use AI.For my detailed coverage on AI and sustainability, see the link here.(11) AI for Scientific Discovery: Artificial Intelligence (AI) is revolutionizing scientific discovery by accelerating the entire research cycle from knowledge extraction and hypothesis generation to automation of experimentation and verification at an unprecedented speed.AI is usually intended to be a purposeful tool. You use AI to achieve some other aim. One of the most exciting and already producing results is the application of AI to the field of science in general. The hope is that via AI, we can discover cures for cancer and unlock all kinds of secrets that heretofore were cloaked in mysteries. AI for scientific discovery is expanding rapidly.I dont want to be a gloomy Gus, but there is a downside to this. AI is construed as a dual-edged sword. AI can be used for good. AI can be used for bad. A famous example of AI dual-use involves researchers who were using AI to detect dangerous chemicals but then realized that evildoers could readily distort that AI to show them new chemicals that could be used to harm people.For my detailed coverage on the use of AI for scientific discovery, see the link here.(12) Artificial General Intelligence (AGI): Although the field of AI has long pursued the kinds of general purpose, human-level abilities captured by the term AGI, the rise of more general capabilities of neural net models has stimulated discussions about directions forward, implications around success, and doubts about pursuing the goalwhich now appears to some observers to be within reach.The news is replete these days with tall tales about artificial general intelligence (AGI). AI makers are stridently claiming they are on the verge of AGI. This is the type of AI that presumably would be fully on par with human intelligence. Not everyone agrees that we are on the cusp of AGI.In addition to AGI, there is artificial superintelligence (ASI) that some believe is on the horizon. ASI consists of AI that would be superior to human intelligence. It could run circles around us humans. Are we ready for AGI? Can we handle ASI? Is existential risk facing us straight head-on?For my detailed coverage on the latest status of AGI and ASI, see the link here.(13) AI Perception vs. Reality: How should we challenge exaggerated claims about AIs capabilities and set realistic expectations?The amount of blarney about AI is astounding. It is everywhere. The public often has no idea of what AI can really do or not do. You can hardly blame them. AI experts, or so-called AI experts, are often making unsupported claims.There is tremendous one-upmanship going on. If one alleged expert says we will have AGI by 2040, another one feels compelled to up the ante by claiming it will be 2035. Nowadays, they have found themselves upping the stakes by claiming that AGI is going to happen this year, namely in 2025. The thing is, they are usually cheating the definition of AGI and moving the goalposts to suit their fancy.For my detailed coverage on the reality versus perception of AI, see the link here.(14) Diversity of AI Research Approaches: It is important to encourage and support research on a variety of AI paradigms, old and new. This includes diverse methodologies (beyond just neural networks) both new and old, interdisciplinary collaboration, and consideration of societal implications.One concern about advancing AI is that there tends to be a groupthink going on. It happens this way. Someone comes up with an AI advance that looks promising. Others jump on the bandwagon. Furthermore, anyone not on the bandwagon is chided as being out of touch.The next thing you know, AI is being moved ahead in just one direction. No one thinks outside the box. They move like a flock of birds. Efforts are underway to break that myopic pathway.For my detailed coverage on AI research approaches, see the link here.(15) Research Beyond the AI Research Community: Expanding AI research to include diverse perspectives and expertise from outside the core AI research.The good news about AI is that it tends to be an interdisciplinary domain.Sure, it is principally viewed as an offshoot of computer science, and it intermixes with areas such as cognitive science and neuroscience, but the likely best way to make progress is by seeding AI into other domains and other domains being seeded into AI. You name it, research in anthropology, art, chemistry, biology, business, history, and so on are all cross-mixes of grand potential.Fresh perspectives ensue. New ideas arise. Inertia tends to be overturned.For my detailed coverage on pertinent research outside of AI that pertains to AI, see the link here.(16) Role of Academia: State-of-the art AI is now largely driven by the private sector, and universities struggle to compete: they need to find a role in the new era of 'big AI.I mentioned earlier that there is a wave or shift toward private sector AI research that has unnerved those in the foundational research realm. The role of academia seeks renewed recognition, particularly if we are to take large swings at new AI theories. Those hitting it out of the ballpark might not be equally supported in private sector make-a-buck arenas.For my detailed coverage on the role of academia and the future of AI, see the link here.(17) Geopolitical Aspects & Implications of AI: The rise of AI is reshaping global power dynamics and the investment priorities of nations, influencing economic, security, and governance structures, while posing challenges to equity and control.AI is likely to be the maker and breaker of international geopolitical power. Imagine that a country invents AGI before any other country. They would be sitting on a gold mine. There is a chance they could aim to rule over other countries using their AGI.For my detailed coverage on the global geopolitical and national stakes in AI, see the link here.Your Next StepsCongrats, you now know about the topmost AI research topics.Ill conclude with a popular adage that Im sure youve heard or seen. They say that the future is what we make of it. For those AI techies that dont give the light of day to the so-called soft topics of AI, such as AI ethics, AI law, AI for good, and so on, I hope you open your eyes to what you are helping to create and bring upon the world.Albert Einstein said it aptly: I believe we are here to do good. It is the responsibility of every human being to aspire to do something worthwhile, to make the world a better place than the one we found.AI researchers should seek to live by those weighty words.