This lawsuit follows one filed last month by Runlayer that accused Rippling of stealing its product ideas. It's a seller- and buyer-beware market warning.

Meta’s new open-weight Muse Glimmer model offers a glimpse of Mark Zuckerberg’s personal superintelligence vision, as well as the emerging divide between AI users can own and access.

After its own AI usage wake-up call, Rippling this week unveiled AI Spend Console, a product that tracks individual and team employee AI spending.

How do we make good, well-informed decisions? This is the central question of author Helen Pearson’s new book Beyond Belief: How Evidence Shows What Really Works. It examines evidence-based decision-making and compares it with “conventional wisdom and questionable opinion”. Pearson is interested in how we can use scientific reasoning to empower decision-making using empirical evidence, rather than choices based on opinion or public sentiment.

Pearson uses human stories and historical narratives to outline “the evidence revolution”, in which academics, policy-makers and civilians have tried to embed evidence into decision-making. She tackles disciplines where evidence is seldom used, such as people management, or where its absence is surprising, like environmental conservation. Throughout Beyond Belief, Pearson asks us to understand what makes for good evidence, and how evidence can be better used in daily life.

An award-winning science journalist and editor at the journal Nature, Pearson is particularly good at establishing clear narratives based on a host of different scientific studies. She begins with the field of medicine, focusing on Iain Chalmers, one of the founders of the Cochrane Collaboration. Formed in 1993, it is considered to be a trailblazer in evaluating evidence-based healthcare. Indeed, the institution has influenced important changes to treatment, including the use of corticosteroids to save the lives of premature babies.

It seems obvious, Pearson suggests, that doctors would choose treatments based on rigorous evidence. Instead, she shows us that evidence-based medicine is a young field that only emerged in the 1970s. One particularly personal example she gives is the standard practice of shaving knee cartilage in meniscal tears – a procedure I nearly underwent in 2015 – which was shown to be no better than exercise therapy after a landmark trial in 2016. Beyond Belief offers a glimpse into how recent, and as-yet fragile, our reliance on evidence-based treatment is; as well as how much opposition it has faced from institutional inertia and scepticism.

The book is not just about medicine. It includes examples ranging from the roll-out of the pioneering Mexican social welfare scheme, PROGRESA, which helped to decrease poverty and improve health and education in the country; to the ineffective use of bat bridges over the UK’s A11 dual-carriageway. She also discusses using evidence to make management decisions, as opposed to relying on corporate wisdom or management consultancy. The examples are often surprising, underscoring Pearson’s central argument that evidence allows for better decisions, but getting people to use good evidence consistently is challenging.

Throughout the book, Pearson is engaging and often witty, while still discussing the complexities of implementing evidence-based best practice. Beyond Belief is highly readable too – even when navigating difficult concepts in meta-analyses. Pearson outlines the difficulties of teaching people how to use evidence, how to design studies well, and how studies can easily be misleading. She presents the enormous challenge of encouraging institutions to use evidence correctly, while remaining hopeful about our direction of travel.

There are places where tensions arise, one being in the chapter on policing. Pearson describes how evidence can be used in effective policing, for example, to support increasing patrols or stop-and-search in crime hot-spots. This reveals an interesting question about what the role of successful policing is – crime reduction, retributive justice or rehabilitation – and whose definition of justice that evidence is being used to support.

In these cases, the author’s arguments would have been strengthened if she had engaged more with the central premise of the book: that evidence always makes for better decisions. There are nuances that arise between evidence-based policy and harm reduction, and broader questions of trust that cause people to rebuke evidence entirely. A more serious conversation about these reservations might have deepened the argument in favour of empirical evidence, particularly in questions of social policy.

This discussion is not entirely absent. One particular strength of Beyond Belief is Pearson’s examination of COVID, drawn from her experience as a Nature editor during the pandemic. In this chapter, close to the book’s end, Pearson presents the difficulties of using evidence-based trials in acutely developing crises, in which scientists and public-health bodies have to adapt quickly to minimize harm. At a time when discussions about public health responses to COVID are so polarized, Beyond Belief provides a nuanced analysis of how we can arrive at a scientific conclusion under immense pressure.

At the heart of this book is Pearson’s passion and advocacy for a scientific approach to decision-making. She includes a call to action, empowering readers to be champions of evidence in their lives. Although only present in the short chapters towards the book’s end, her anecdotes about childcare, artificial intelligence and using your vote to support evidence through democracy, are useful reference tools. She also succeeds in her goal of starting a conversation about how we can best use evidence.

Ultimately, Beyond Belief sets out to demonstrate the ways in which evidence enters our lives and improves them. Pearson asks us to advocate for science in a world of increasing misinformation, and to challenge conventional wisdom while equipping us with the tools to do so. For that alone, her book is well worth reading.

• 2026 Princeton University Press 368pp £25 hb

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India’s first privately funded theoretical-physics research institute is set to open in the coming months in Mumbai. The Lodha Theoretical Physics Institute (LTPI), which was announced on 26 May, will be led by quantum physicist Jainendra Jain who is based at Pennsylvania State University in the US.

Jain, who will keep his position at Penn State, says that the new physics institute is expected to have at least 10 full-time faculty members, around 30 postdocs as well as long- and short-term visitors from around the world.

The first LTPI postdoc has already been recruited and Jain expects several visiting faculty members at LTPI by early 2027, who will be spending significant time in Mumbai. LTPI plans to hire 3–5 full time faculty members within the next three years.

LTPI is intentionally designed to be smaller and more focused than a typical research institute and will initially focus on three or four areas, one of which is strongly interacting quantum matter.

“We believe that this scale will allow the institute to respond nimbly to emerging scientific opportunities,” Jain told Physics World, adding that quantum matter “promises to continue to be a fertile ground for new transformative discoveries”.

LTPI is being set up by Mumbai-based Lodha Foundation – the philanthropic arm of India’s Lodha Group, which is one of the country’s largest real estate and construction companies.

In August the Lodha Foundation established the Lodha Mathematical Sciences Institute in Mumbai – India’s first privately funded mathematical sciences institute.

‘A destination of choice’

Privately funded research institutes are still a novelty in India, with most research, especially in basic science, carried out in publicly funded research institutes.

Compared to government-funded institutes, Jain says that LTPI’s goal “is to create an environment where researchers can devote the maximum possible fraction of their time and energy to science”.

To that end, Jains adds that administrative burdens and bureaucracy that often accompany publicly funded organizations will be kept to a minimum, with the institute having the flexibility to move into new areas.

LTPI will not grant academic degrees, and its members will not have mandatory teaching obligations.

Researchers at LTPI will also not be required to secure external funding to sustain their research programs, which will help them pursue long-term scientific questions.

“Our goal is to build an institute in India that stands alongside the finest theoretical physics centres and becomes a destination of choice for outstanding physicists from around the world,” adds Jain.

Sunil Mukhi from the publicly funded Indian Institute of Science Education and Research in Pune, says that the lack of government support for theoretical sciences as well as visitor programmes means that the LTPI will be a “huge benefit to the community”.

“Publicly funded institutes are subject to lots of rules and regulations that make it more complicated to function,” he says. “Government funding for [visitors] is very difficult to get because the value and necessity of visitor programmes is not understood by most of our agencies,” he says.

Mukhi adds it is “great to see industrialists stepping up” and that they are “now in a position to play a role”. “The talent is already there and only a well-funded, flexible, creative environment is needed,” adds Mukhi.

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John Hill has become director of the Brookhaven National Laboratory in Long Island, New York, after serving as interim lab director since September. Hill will now oversee Brookhaven’s 3000-strong team of scientists, engineers and technicians as well as manage the lab’s annual $900m budget.

Brookhaven opened in 1947 as one of the first three US national labs, the others being Argonne and Oak Ridge. Brookhaven carries out a wide range of research in the physical, biomedical and environmental sciences and is home to seven Nobel-prize-winning discoveries.

Brookhaven operated the Relativistic Heavy Ion Collider (RHIC) until it shut down in February. RHIC collided heavy nuclei such as gold and copper to produce a quark-gluon plasma – a state of matter thought to have been present in the very early universe.

In 2020, Brookhaven was chosen to host the next-generation Electron-Ion Collider (EIC). Costing about $2bn, the EIC will smash together electrons and protons to probe the strong nuclear force and the role of gluons in nucleons and nuclei.

Building the EIC involves revamping the RHIC accelerator as well as adding an electron ring and other components with the first experiments starting the 2030s.

As well as RHIC and the EIC, Brookhaven is also home to other big-science projects including the National Synchrotron Light Source II, which opened in 2015 at a cost of $912m.

A Brookhaven career

With a PhD in physics from the Massachusetts Institute of Technology, Hill joined Brookhaven as a postdoc in 1992 before leading the lab’s X-ray scattering group from 2001 to 2013.

He then became deputy associate laboratory director for energy and photon sciences until becoming the lab’s deputy director for science and technology from 2023 to 2025.

In September 2025 he became interim director following the resignation of the theoretical physicist JoAnne Hewitt.

In the role, Hill will also become president of Brookhaven Science Associates – a partnership between Stony Brook University and the science and tech firm Battelle – that manage and operate Brookhaven on behalf of the US Department of Energy.

Hill notes that he is “very excited” to lead the lab in the coming years. “Brookhaven is entering a defining decade, and I’m honoured to take on this role at this time,” he says. “The vision we have for our future is a powerful one, including delivering the nation’s next particle collider and advancing science across a range of critical areas.”

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The lines between separate scientific disciplines are becoming more blurred. Solving today’s problems often requires teams of scientists from a range of specialisms. But multidisciplinary collaboration also has challenges, in particular the need to “speak the same language”, ask the “right” questions and be familiar with techniques and knowledge that exist in other fields.

To see the importance of finding a common language look no further than the rapid uptake of large language models (LLMs) such as ChatGPT. LLMs can be convenient research aids, but the information provided by them is not always accurate. We can ask LLMs questions about another field, but without existing domain knowledge we cannot always tell if the answers are reliable.

Getting up to speed with a new research field can be tricky – it’s difficult to understand everything fully, but tempting to think that you do. There’s a parallel with sport where it might sound reasonable, say, to assume that mixed martial arts (MMA) fighters can easily become boxers. However, the evidence suggests that MMA fighters often struggle against professional boxers even though fist fighting uses a subset of the skills needed to be successful in MMA.

Back in academia, it’s common to get pushback from “real experts” whenever grant proposals or papers drift too far outside one’s own comfort zone. Nevertheless, discipline mixing is needed more than ever. Today’s problems often straddle different scientific disciplines: how to treat large, complex datasets, for example, is a common challenge in many different fields.

Look up at the stars and not (just) down at your tea

We realized this recently in our work at Queen’s University Belfast, which has been pushing for researchers to share their data analysis strategies with colleagues in other fields. In our case, we had been collaborating with Yicong Li at the Institute for Global Food Security on infrared and ultraviolet-visible spectroscopy and machine-learning models for monitoring the freshness of fish, which required only a few samples for analysis.

However, many food studies need hundreds or thousands of samples to be analysed and class imbalances can quickly arise in which some types of foodstuff have more examples than others. This can then lead to training datasets that do not produce predictive models. One example is tea, which Li has been investigating recently, again via spectroscopy and machine learning, using many samples from all over the world.

Li was trying oversampling, which creates synthetic data to equalize class imbalances. Yet over in the Queen’s physics department, we discovered another strategy was being used to classify problems in astrophysics. Matt Nicholl and PhD student Xinyue Sheng had been working on predicting the classes of energetic cosmic explosions, based on an image of the galaxy where they occurred. They wanted to train their model to find particularly rare classes, so their training set had the same problem: there were only a handful of examples of some classes of interest.

In addition to oversampling, they were also using a “weighted loss function” in their training, in which weights were inversely proportional to the number of examples in a given class. Their approach led to a substantial improvement in their astrophysics application, but it turns out the basic idea is completely general in nature and can be just as easily applied to tea.

Sleeping beauties

Knowledge exchange does not only concern data, but sometimes a whole set of ideas. An interesting study of citation metrics in 2015 by researchers at Indiana University found that there is a class of papers that receive very little attention for years before suddenly shooting skywards with a deluge of citations. Notably, these “sleeping beauty” papers include Albert Einstein, Boris Podolsky and Nathan Rosen’s work in 1935 examining non-locality in quantum mechanics, which led to John Bell’s theorem in 1964 and ignited significant interest in the original “EPR” paper.

Such citation trends can arise because the papers’ findings are adopted by researchers in a different field. Other similar instances include work in the 1930s and 1940s on hydrophobic theory, which describes how certain substances minimise their contact with water. Yet perhaps the sleepiest of sleeping beauties is the principal component analysis (PCA) work by Karl Pearson, which slumbered for over 100 years before “awakening” in the early 2000s.

PCA – a technique that simplifies complex datasets by reducing the number of variables while minimizing information loss – had already been gaining traction during the 1980s and 1990s when matrix calculations became easy for computers alongside the development of statistical software packages and open scripting environments. In research papers published today it would be unusual not to see PCA used as an exploratory tool for multivariate dataset analysis.

As these examples show, it’s crucial that communication channels are open between varying fields. However, too many academic researchers can get siloed. Interdisciplinary science hubs are one way to break down barriers, acting as spaces to exchange ideas between scientists.

One example that we have been involved with is Smart Nano NI, which is a consortium of universities and photonics-based companies in Northern Ireland. It recently released TITAN, a bio-process analysis system based on gold nanostructured chips, for real-time bio-analysis. Smart Nano NI is now moving from benchtop to backpocket, looking to develop fully miniaturized sensing devices by integrating different kinds of photonic components like lasers, filters and detectors, all on the same chip.

Elsewhere, centres for doctoral training – such as the Photonic Integration and Advanced Data Storage programme with the University of Glasgow – bring together groups of PhD students to work on various projects under a common theme. These schemes not only foster new ideas with the student cohort but bring together academics to bridge different parts of research. Either way, we are getting people talking and interested in emerging scientific questions.

So if you are sitting on a problem, there might be a chance that someone in a different field has solved it or at least offered the tools to do so. As our sky-gazing friends might say, “There is nothing new under the Sun.”

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Databricks has remade its image into an AI company and has published research on the cost savings of open weight AI models for coding.

The all-cash deal gives MoEngage access to technology that assigns AI agents to individual customers.

Anthropic’s new Claude Tag brings an always-on AI teammate to Slack. But beyond productivity, the feature is a strategic play to capture organizational context, institutional knowledge, and enterprise workflows.

Stockholm-based startup Fika Jobs is building a video-first hiring platform that combines AI interview agents with short-form video profiles, creating something that feels like a cross between LinkedIn and TikTok.

Deductive AI, a startup that uses AI to catch and resolve bugs in software, was founded just three years ago.

AWS is in talks to sell its chips to other data centers. CEO Andy Jassy has said this represents a $50 billion opportunity for the company.

The Canadian pension giant will acquire an 8.2% stake in CtrlS, a tech giant that operates more than 15 data centers across India.

Anthropic's popularity with business users is growing so well that the latest beef with the government might actually boost it, data from Ramp suggests.

Hewlett Packard Enterprise’s (HPE) new virtualization software promotion will likely pique the interest of end users and resellers who are unhappy with Broadcom's pricing of VMware.

During its HPE Discover event in Las Vegas this week, HPE announced that customers could use its “HPE Morpheus Software—VM Essentials” offering for free for “up to one year,” per a press release. HPE’s website describes its virtualization platform as a “VMware alternative.” It includes a hardware virtual machine (HVM) hypervisor and unified management and lets users "manage VMware ESXi and HVM clusters from one console and migrate when you’re ready,” HPE’s website says.

“New VM Essentials customers can receive up to one free year of licenses for VM Essentials, a year of HPE Zerto for $1 to support non-disruptive migration to HPE virtual machines, and 0 percent interest on software through HPE Financial Services,” HPE’s announcement reads, referring to HPE’s group for helping IT teams manage funding.

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Hewlett Packard Enterprise’s (HPE) new virtualization software promotion will likely pique the interest of end users and resellers who are unhappy with Broadcom's pricing of VMware.

During its HPE Discover event in Las Vegas this week, HPE announced that customers could use its “HPE Morpheus Software—VM Essentials” offering for free for “up to one year,” per a press release. HPE’s website describes its virtualization platform as a “VMware alternative.” It includes a hardware virtual machine (HVM) hypervisor and unified management and lets users "manage VMware ESXi and HVM clusters from one console and migrate when you’re ready,” HPE’s website says.

“New VM Essentials customers can receive up to one free year of licenses for VM Essentials, a year of HPE Zerto for $1 to support non-disruptive migration to HPE virtual machines, and 0 percent interest on software through HPE Financial Services,” HPE’s announcement reads, referring to HPE’s group for helping IT teams manage funding.

Read full article

Comments

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Salesforce says it wants to use Fin's team and technology to improve Agentforce, its existing enterprise platform that businesses can use to build custom AI agents that automate tasks.

Orbio announces $21 million Series A in round led by Dawn Capital.

The partnership will see TCS creating a business unit focused on deploying Anthropic's AI models to its customers.