💡 TL;DR: We’ve released new minor versions of deepagents & deepagentsjs, featuring async (non-blocking) subagents, expanded multi-modal filesystem support, and more. See the changelog for details. Async subagents Deep Agents can now delegate work to remote agents that run in the background. As opposed to the existing

The rise of AI over the past two years or so has often been framed as a high stakes race. Bigger models, unbelievable company valuations, more compute and bigger datacenters. […] The post AI Is Running Into a $7 Trillion Wall appeared first on BigDATAwire.

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Phonlamai Photo/Shutterstock In just a few years, generative artificial intelligence (gen AI) has brought about significant changes in many industries from healthcare to education, entertainment to finance, and even law. The use of gen AI in court verdicts poses significant risks to justice. Erroneous outcomes generated from “hallucinated” information, discriminatory decisions and lack of transparency are all concerns when this technology is introduced to courtrooms. But already a number of judges around the world have used it in decision-making and judgment writing. This is why some jurisdictions, including the UK, have issued guidelines for judges regarding AI use. ‘Hallucinated’ cases are affecting lawyers’ careers – they need to be trained to use AI Broadly, the guidelines suggest judges might use AI as a tool to conduct preparatory works such as drafting summaries of long documents, translating legal documents, identifying legal precedents or enhancing readability of documents. They recommend against the application of it for core judicial functions, including decision-making. Recently, some senior judicial leaders have opined that AI might be used to decide “low-stakes” or less-complex cases with adequate precautions, such as keeping a human judge in the loop. In a November 2024 speech, the UK’s second most senior judge, Geoffrey Vos, spoke of a “spectrum” of legal decisions that AI might soon make, or help make. Vos said the use of AI for “broadly mechanical decisions, like those about the amount of a pension or benefits, or the calculation of personal injury damages and loss of earnings” would likely save money and time. But he called for discussion on whether such use would violate essential human rights. A year later, Vos again called for “serious debate” about what rights humans should have protected in this context. And he urged that AI be “used responsibly, effectively and safely in legal systems and processes”. AI has long been discussed as a threat to jobs and livelihoods. But what’s the reality? In this new series, we explore the impact it is already having on different occupations – and how people really feel about their AI assistants. A number of jurisdictions are testing or using AI in such “mechanical” cases already. Estonia uses a semi-automated small-claims system in civil proceedings for monetary claims up to €7,000 (£6,100), with human clerks overseeing the process. Frankfurt District Court in Germany has tested an AI system named Frauke to deal with air passenger rights lawsuits. Frauke analyses earlier cases and rulings to create pre-configured draft judgments. Judges assemble final verdicts from these texts following their ruling, significantly reducing the time spent drafting. Taiwan piloted an AI-powered tool to assist courts by producing ruling notices for Driving Under Influence cases, or aiding and abetting in fraud cases. The AI system generates a complete draft ruling including the facts, legal reasoning, citations and final verdict. The judge reviews this draft and, upon approval, can issue it as the official judgment, with or without modifcations. It is evident from these examples that the key motivation to replace human judges in a certain category of cases is efficiency. As a result, a few other jurisdictions are also exploring the scope of integrating gen AI to adjudicate certain litigation without human judges. The cost of using gen AI as judge Courts are overburdened, and technology like gen AI promises consistency and efficiency. But it would mark a significant change of centuries-old practice. And it risks undermining what some legal scholars argue is a fundamental principle of justice: the right to be judged by a human being. Court adjudication is not only about reaching a decision. It is about a holistic and fair process that includes the right to be heard – presenting defence, weighing competing narratives, and exercising judgment in light of law and equity. Algorithmic tools, no matter how advanced, do not hear or “understand” even their own output, let alone human values or changing social contexts. Gen AI cannot recognise suffering, credibility, remorse or vulnerability like a human. That alone makes it unfit to sit in a judge’s seat. Some legal scholars argue the right to be judged by a human is a fundamental principle of justice. Korawat photo shoot/Shutterstock Categorising cases as simple or complex may look pragmatic, but it is both legally and morally dangerous. What counts as a “simple, routine or mechanical” case is itself a human decision. Legal disputes over compensation or benefits may appear straightforward on paper, yet carry significant consequences for the person bringing the case. Allocating such cases as appropriate for algorithmic adjudication risks creating a two-tier justice system – in which one group of citizens gets to present their case before a human judge, while others are handled by machines. Only the former, I would argue, are exercising their right to a fair hearing and trial before an independent and impartial tribunal, as protected under Article 6 of the European Convention on Human Rights. Additionally, the efficiency argument may become illusory. Algorithmic systems like gen AI require continuous human oversight, auditing and rectification. Hallucination or mistakes, whether from flawed design or biased training data, can completely negate the claimed benefits. Public trust matters in all legal systems. If people lose trust in automated decisions, appeals will increase – adding to the existing backlog of cases. Emerging technology such as gen AI may be suitable to manage court administration and reducing clerical burdens. But substituting human judges, even in supposedly low-stakes cases, undermines basic principles of justice. Efficiency should not come at the expense of the values the justice system exists to protect. Raisul Islam Sourav does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

The company says its "Earth System of Record" will be instrumental in connecting AI more securely to the physical world.

Arcade is the MCP runtime for production agents, delivering secure agent authorization, reliable tools, and governance. This integration gives your agents access to Arcade’s collection of 7,500+ agent-optimized tools through a single secure gateway.

Dark Reading's Kelly Jackson Higgins shares insights on the past, present, and future of cybersecurity after attending RSAC 2026 Conference.

The deals are dependent on the vendor's commercial success.

As AI dominated RSAC 2026, CISOs and industry leaders debated its role in security, from agentic applications to the challenges of scaling human involvement in decision-making.

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How the product development processes we took for granted are being disrupted. Examples from Spotify, Linear, Claude Code and more.

In December, The Conversation hosted a webinar on AI’s revolutionary role in drug discovery and development. Science and technology editor Eric Smalley interviewed Jeffrey Skolnick, eminent scholar in computational systems biology at Georgia Institute of Technology, and Benjamin P. Brown, assistant professor of pharmacology at Vanderbilt University. Skolnick has developed AI-based approaches to predict protein structure and function that may help with drug discovery and finding off-label uses of existing drugs. Brown’s lab works on creating new computer models that make drug discovery faster and more reliable. Below is a condensed and edited version of the interview. Let’s start with the big picture. How is AI changing biomedical research and drug discovery, and what is the potential we are talking about? Skolnick: The upside, potentially, is very large. One of the frustrating things about drug discovery is that, in spite of the fact that the people doing it are extraordinarily intelligent and have done an extraordinarily good job, the success rate is very low. About 1 in 5 drugs will have negative health effects that outweigh its benefits. Of the ones that pass, roughly half don’t work. In drug development, there are several key issues: Can you predict which target is driving a particular disease? Once this target is identified, how can you guarantee the drug is going to work and isn’t simultaneously going to kill you? These are outstanding problems in drug discovery in which AI can play an important, though not 100% guaranteed, role. Unlike us, AI can look at basically all available knowledge. On a good day it makes strong and true connections called “insights,” and on a bad day it does what is called “hallucinating” and sees things that are weak and probably false. Eric Smalley interviews Jeffrey Skolnick and Benjamin P. Brown. At the end of the day, many diseases do not have a cure. Most diseases are maintained, such as high cholesterol or autoimmune conditions. A treatment for cancer might buy you five years, and now you’re in Stage 4 and you’ve exhausted all the standard care drugs. AI can play a role to suggest alternatives where there are none. Let’s give some basic definitions here. When we use the word drug, we’re talking about a wide range of therapies. Can you explain the range – we’ve got small molecule drugs, biologics, gene therapies, cell therapies. Brown: We have fairly large molecules in our bodies called proteins. They are like machines that carry out specific functions and interact with one another. Oftentimes, when we’re trying to treat disease, we’re trying to alter functions of specific proteins. Many drugs, like aspirin and Tylenol, are small molecules that can fit into a protein and change its function. Fundamentally, drugs don’t have to just interact with proteins, but this is a major way in which our current repertoire of medications work. There are also proteins that act like drugs, such as antibodies. When you receive a vaccine for a virus, your body is basically given instructions on how to develop antibodies. These antibodies will target some part of that virus. Your body is creating these big molecules, much bigger than aspirin, to go and interact with foreign proteins in a different way. Gene therapy is a larger step beyond that. So these modalities – molecule, protein, antibody or gene – are very different types of molecules. They have different scales and rules, so the way you approach designing and discovering them various widely. Can you briefly explain artificial neural networks, and what the “deep” in deep learning means? Skolnick: AlphaFold, developed by DeepMind, involved understanding how neural networks worked. They built a network with a lot of inputs, which are stimuli, and outputs with different weights, similar to how your brain actually works. These simple connections, or neurons, have reinforcement learning. They also created sophisticated neural networks, such as transformers, which do specific things like a special-purpose tool that can learn, and they added a mechanism called “attention,” which amplifies critical details. Super neural networks with transformers is what we call deep learning. These now have literally billions, if not trillions, of parameters. Essentially, these machines can learn higher order correlations between events, meaning the patterns of conditional interactions that depend on the properties of multiple things simultaneously. In these higher order correlations, AI has the potential to see previously unknown things that are embedded in petabytes (a unit of data equivalent to half of the contents of all U.S. academic research libraries of biological data. AlphaFold, which predicts three-dimensional, bioactive forms of a protein, has millions of sequences and a couple of hundred thousand structures. It can tell you, based on a particular pattern, what small molecule to design that sticks to a protein to induce some kind of structural shift. How is this technology being used in biomedical research to understand molecular dynamics or, essentially, the biological processes involved in health and disease? Brown: In 2013, there was a Nobel Prize for molecular dynamics simulations, computational tools that help you understand the motions of molecules as they move according to physics. There’s a huge body of scientific research built around those ideas. AI and deep learning are large right now, but it’s worth mentioning that for the last decade and a half, people have been using much smaller machine learning algorithms to help design drugs. A lot of the ideas, such as [using machine learning for virtual screening], are not new and have been in practice for a while. With AlphaFold’s technologies to help people design proteins and predict their structure, we’ve changed how we think about a lot of these problems. We have this new repertoire of approaches to build ideas around and to start thinking about drug discovery. From 20 years ago to now, what has today’s AI technology done in terms of scale of change in this process? Skolnick: A lot of diseases, like cancers, are caused by a collection of malfunctioning proteins. AI now allows us to start to think conceptually about how these diseases are organized and related to each other. Diseases tend to co-occur. For example, if you have hyperthyroidism, you’re very likely to develop Alzheimer’s. Kind of weird, right? We can look at pieces, but AI can look at all the information, integrate the collective behavior and then identify common drivers. This allows you to construct disease interrelationships which offer the possibility of broad spectrum treatments that could treat whole collections of diseases rather than narrow-spectrum treatments. Relatedly, AI also can help us understand disease trajectories. Diseases that tend to co-occur often present themselves consecutively. You have disease 1, it gives you disease 2, then gives you disease 3. This suggests that if you go back to the root with disease 1, you may be able to stop a whole bunch of stuff. You can’t analyze millions of trajectories and millions of data without a tool, so you couldn’t do this before. This holds a lot of promise, but one also must be careful not to overpromise. It will help, it will accelerate, but it is not a substitute yet for real experiments, real clinical validation and trials. Jeffrey Skolnick receives funding from the National Institute of Health and the Ovarian Cancer Institute. Benjamin P. Brown receives funding from the National Institutes of Health.

AI dominated RSAC 2026 Conference, but it's still the humans in cybersecurity who matter most.

The foundation focuses on providing essential tools to develop open, interoperable infrastructure for agentic AI applications.

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The software company slowed spending, but now finds itself falling behind

In a quarter when nearly two-thirds of global venture capital went to just four companies, it’s easy to lose track of the many other companies getting funding to tackle interesting problems. We spotted five companies in just the past month working on issues from cleaner ferries and trains to foundational AI for the crop industry.

For years, the National Football League offseason was a period defined by information asymmetry. While front offices sat behind “glass walls” in war rooms, armed with proprietary Next Gen Stats and sophisticated modeling tools, the average fan was left to navigate a fragmented landscape of mock drafts, cap calculators, PDF guides and Twitter rumors. Last […] The post How the NFL is using Amazon Quick to humanize the offseason appeared first on SiliconANGLE.

Issam Hijazi launched UpScrolled after users alleged censorship on other platforms. Nine months later, its user base is soaring—while Hijazi tries to catch up with his own success.