Thousands of abusive videos were produced last year--that researchers know of. In 2025, new data show, the volume of child pornography online was likely larger than at any other point in history. A record 312,030 reports of confirmed child pornography were investigated last year by the Internet Watch Foundation, a U.K.-based organization that works around the globe to identify and remove such material from the web. This is concerning in and of itself. It means that the overall volume of child porn detected on the internet grew by 7 percent since 2024, when the previous record had been set.

We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We identify a parameter-dependent effective stochastic differential equation (eSDE) in terms of physically meaningful coarse mean-field variables through a deep-learning ResNet architecture inspired by numerical stochastic integrators. We construct an approximate effective bifurcation diagram based on the identified drift term of the eSDE and contrast it with the mean-field SIS model bifurcation diagram. We observe a subcritical Hopf bifurcation in the evolving network's effective SIS dynamics, that causes the tipping point behavior; this takes the form of large amplitude collective oscillations that spontaneously -- yet rarely -- arise from the neighborhood of a (noisy) stationary state. We study the statistics of these rare events both through repeated brute force simulations and by using established mathematical/computational tools exploiting the right-hand-side of the identified SDE. We demonstrate that such a collective SDE can also be identified (and the rare events computations also performed) in terms of data-driven coarse observables, obtained here via manifold learning techniques, in particular Diffusion Maps. The workflow of our study is straightforwardly applicable to other complex dynamics problems exhibiting tipping point dynamics.

We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipping points in the emergent behavior of complex systems, and (b) characterizing probabilities of rare events (here, catastrophic shifts) near them. Our illustrative example is an event-driven, stochastic agent-based model (ABM) describing the mimetic behavior of traders in a simple financial market. Given high-dimensional spatiotemporal data -- generated by the stochastic ABM -- we construct reduced-order models for the emergent dynamics at different scales: (a) mesoscopic Integro-Partial Differential Equations (IPDEs); and (b) mean-field-type Stochastic Differential Equations (SDEs) embedded in a low-dimensional latent space, targeted to the neighborhood of the tipping point. We contrast the uses of the different models and the effort involved in learning them.

We’re hitting a tipping point for artificial intelligence: With ChatGPT and other AI models that can communicate in plain English, write and revise text, and write code, the technology is suddenly becoming more useful to a broader population of people. This has huge implications. The ability to produce text and code on command means people are capable of producing more work, faster than ever before. Its ability to do different kinds of writing means it’s useful for many different kinds of businesses. Its capacity to respond to notes and revise its own work means there’s significant potential for hybrid human/AI work. Finally, we don’t yet know the limits of these models. All of this could mean sweeping changes for how — and what — work is done in the near future.

Investment in artificial intelligence startups hit a record high of $59 billion in 2021, up from $28 billion in 2020, according to Crunchbase data. Venture investment in AI startups will only continue to rise in the next five years. That's because AI has finally reached a tipping point where it is powerful and affordable enough to make a real economic impact on diverse industries. AI is a broad category, but generally encompasses all "intelligent" software that can learn from itself in some capacity. This can include machine-learning platforms and the software used by robots, and self-driving vehicles to interpret the environments around them.

A predictive model of a polarized group, similar to the current U.S. Senate, demonstrates that when an outside threat – like war or a pandemic – fails to unite the group, the divide may be irreversible through democratic means. Published today in the Proceedings of the National Academy of Sciences as part of a Dynamics of Political Polarization Special Feature, the model identifies such atypical behavior among the political elite as a powerful symptom of dangerously high levels of polarization. "We see this very disturbing pattern in which a shock brings people a little bit closer initially, but if polarization is too extreme, eventually the effects of a shared fate are swamped by the existing divisions and people become divided even on the shock issue," said network scientist Boleslaw Szymanski, a professor of computer science and director of the Army Research Laboratory Network Science and Technology Center (NeST) at Rensselaer Polytechnic Institute. "If we reach that point, we cannot unite even in the face of war, climate change, pandemics, or other challenges to the survival of our society." The model – essentially a game that simulates the views of 100 theoretical legislators over time – allowed researchers to dial up party identity, intolerance for disagreement, and extremism to levels such that almost no degree of shock could unite the legislative group. In some situations, the simulation revealed that even the strongest shock fails to reverse the self-reinforcing dynamics of political polarization.

An AI identifies a person while they are walking on the street. As the AI market expands and AI use cases permeate every industry, every once in a while I hear the question - when will the AI singularity occur? For those who are not familiar with this term - the AI singularity refers to an event where the AIs in our lives either become self aware, or reach an ability for continuous improvement so powerful that it will evolve beyond our control. While this is a reasonable concern in the future, I argue that there are much more pressing concerns in the present - in particular that AI has reached a Tipping Point. A tipping point is a state where a technology grows and permeates our lives very rapidly, building upon itself.

A technology reaches a tipping point when it hits three milestones: First, it becomes technically feasible to accomplish important tasks with it. Second, it becomes cheap enough to use for those tasks. And third, critically, it becomes sufficiently easy for non-experts to build products with it. Passing those milestones is a great indicator that a technology is poised to spread like wildfire. At this year's Embedded Vision Summit (coming up online May 25-28), we're seeing clear evidence that embedded vision has reached this point.

The "Report for the State of RPA and Smart Automation" interviewed more than 1,000 business executives in North America and found that while 75.3 percent believe automation will make them more competitive, significant disparities exist between industries – with public sector and surprisingly, technology companies lagging significantly when it comes to adoption. While more than half of businesses in North America have already implemented some type of automation solution, such as RPA and AI, the research uncovered notable differences between industries. For instance, nearly nine in 10 manufacturing organizations have already adopted some form of intelligent automation, compared to less than three in 10 public sector organizations. Despite these identified barriers to overcome, nine in 10 organizations that have not yet implemented RPA and AI-based automation solutions report having sufficient internal technical competencies to do so, showing that technical implementation is no longer a significant hurdle for most organizations. "Intelligent automation marks a quantum leap for humanity, and like the early stages of the internet, companies that do not adapt to this new reality risk becoming obsolete," said Daniel Newman, founder and principal analyst at Futurum Research.

AI is the greatest IT disruption of our time, promising to transform society and industry. I've never seen a technology with as much potential to boost the security, health and prosperity of our country. It's estimated to make an economic impact measured in trillions of dollars. The U.S. federal government has been moving quickly, especially this past year, to help advance our nation's adoption of this transformative technology. From the White House to agency leaders and department heads in dozens of federal organizations, the government is acutely aware of the competitive international environment, with more than 35 countries that have already announced AI strategies.