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  • AI study finds stroke may make the opposite brain hemisphere look younger, offering new clues on recovery

    AI study finds stroke may make the opposite brain hemisphere look younger, offering new clues on recovery

    A new study in The Lancet Digital Health suggests the brain can respond to stroke in a surprising way. Researchers at the USC Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) found that people with severe physical impairments after a stroke may show signs of a “younger” brain structure in areas that were not damaged. This appears to reflect how the brain adapts and reorganizes itself after injury.

    The research was conducted as part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery Working Group. Scientists analyzed brain scans from more than 500 stroke survivors collected across 34 research centers in eight countries. By applying deep learning models trained on tens of thousands of MRI scans, the team estimated the “brain age” of different regions in each hemisphere and examined how stroke affects both structure and recovery.

    “We found that larger strokes accelerate aging in the damaged hemisphere but paradoxically make the opposite side of the brain appear younger,” said Hosung Kim, PhD, associate professor of research neurology at the Keck School of Medicine of USC and co-senior author of the study. “This pattern suggests the brain may be reorganizing itself, essentially rejuvenating undamaged networks to compensate for lost function.”

    AI Reveals Brain Rewiring After Stroke

    To carry out the analysis, researchers used a type of artificial intelligence called a graph convolutional network. This system estimated the biological age of 18 brain regions based on MRI data. They then compared this predicted age with each person’s actual age, a measure known as the brain-predicted age difference (brain-PAD), which serves as an indicator of brain health.

    When these brain age measurements were compared with motor function scores, a clear pattern emerged. Stroke survivors with severe movement impairments, even after more than 6 months of rehabilitation, showed younger-than-expected brain age in regions opposite the site of injury. This effect was especially strong in the frontoparietal network, which plays an important role in movement planning, attention, and coordination.

    “These findings suggest that when stroke damage leads to greater movement loss, undamaged regions on the opposite side of the brain may adapt to help compensate,” Kim explained. “We saw this in the contralesional frontoparietal network, which showed a more ‘youthful’ pattern and is known to support motor planning, attention, and coordination. Rather than indicating full recovery of movement, this pattern may reflect the brain’s attempt to adjust when the damaged motor system can no longer function normally. This gives us a new way to see neuroplasticity that traditional imaging could not capture.”

    Large-Scale Data Reveals Hidden Patterns

    The study relied on ENIGMA, a global collaboration that combines data from more than 50 countries to better understand the brain across different conditions. By standardizing MRI data and clinical information from many research groups, the team created the largest stroke neuroimaging dataset of its kind.

    “By pooling data from hundreds of stroke survivors worldwide and applying cutting-edge AI, we can detect subtle patterns of brain reorganization that would be invisible in smaller studies. These findings of regionally differential brain aging in chronic stroke could eventually guide personalized rehabilitation strategies,” said Arthur W. Toga, PhD, director of the Stevens INI and Provost Professor at USC.

    Toward Personalized Stroke Recovery

    The researchers plan to continue this work by following patients over time, from the early stages after a stroke through long-term recovery. Tracking how brain aging patterns and structural changes evolve could help doctors tailor treatments to each person’s unique recovery process, with the goal of improving outcomes and quality of life.

    Learn more about associations between contralesional neuroplasticity and motor impairment by viewing this video made by the Stevens INI.

    The study, “Deep learning prediction of MRI-based regional brain age reveals contralesional neuroplasticity associated with severe motor impairment in chronic stroke: A worldwide ENIGMA study,” was funded by the National Institutes of Health (NIH) grant R01 NS115845 and supported by international collaborators from institutions including the University of British Columbia, Monash University, Emory University, and the University of Oslo.

  • Hong Kong PolyU researchers unveil AI tool to score large language model personality, with implications for business compliance and education

    Large language models have become a default interface for many AI products, but researchers still struggle to describe their behaviour in a consistent, measurable way. A team at The Hong Kong Polytechnic University says it has built a system that aims to quantify an LLM’s personality based on linguistic output.

    The tool, called Language Model Linguistic Personality Assessment, or LMLPA, is designed to translate model responses into numerical scores tied to personality traits. The researchers describe it as a step toward making model behaviour easier to compare across systems and deployments.

    How the LMLPA system works

    LMLPA combines two components: an adapted version of the Big Five Inventory and an AI rater that grades the model’s answers. The process focuses on patterns such as wording, style and other language features found in generated text.

    By using a standardised questionnaire structure, the approach attempts to bring more consistency to assessments that often rely on subjective impressions. The researchers say the outcome is a data-driven profile that can be tracked and tested across different prompts and settings.

    Why personality metrics could matter

    Developers and organisations increasingly want AI assistants to behave predictably in sensitive contexts, including classrooms, customer support and internal decision workflows. The team argues that quantifying communication tendencies could help tailor a model’s tone and interaction style to a specific use case.

    The researchers also point to potential value in governance and oversight, where firms are under pressure to document how AI tools behave and how risks are managed. They suggest that structured behavioural metrics could complement existing evaluation methods focused on accuracy and safety.

    From research to compliance applications

    PolyU said the work has also informed a business compliance platform that uses natural language processing to analyse large volumes of reports and other text. In that context, automation is intended to speed up data collection, analysis and insight generation for reporting tasks.

    The study, led by Prof. Lik-Hang Lee of PolyU’s Department of Industrial and Systems Engineering, was published in the journal Computational Linguistics. The researchers position LMLPA as part of a broader effort to align AI systems with human values and practical operational needs.

  • AI-built molecular atlas maps Alzheimer’s brain beyond amyloid plaques, pointing to overlooked metabolic shifts

    AI-built molecular atlas maps Alzheimer’s brain beyond amyloid plaques, pointing to overlooked metabolic shifts

    Researchers at Rice University have created a label-free molecular atlas of the Alzheimer’s brain in an animal model, using laser-based imaging paired with artificial intelligence. The work aims to clarify how the disease emerges and spreads beyond what standard pathology typically captures.

    The study used hyperspectral Raman imaging, an advanced form of Raman spectroscopy that reads chemical fingerprints in tissue without dyes or fluorescent tags. By scanning brain slices at high resolution, the team generated a detailed chemical map designed to reflect the brain’s native state.

    What the imaging revealed

    Analysis indicated that Alzheimer’s-linked chemical changes were not limited to amyloid plaques. Instead, the alterations appeared across multiple brain regions, with uneven patterns that could help explain why symptoms develop gradually and differ between individuals.

    To handle the large dataset, the researchers applied both unsupervised and supervised machine learning methods. Unsupervised tools grouped tissue by molecular similarity, while supervised models helped distinguish Alzheimer’s-affected samples from controls across different regions.

    Metabolic signals in key regions

    Beyond protein-related pathology, the maps pointed to broader metabolic differences, including shifts in cholesterol and glycogen signals. The strongest contrasts were reported in brain regions central to memory and cognition, including the hippocampus and cortex.

    The authors argue that these molecular patterns support a wider view of Alzheimer’s as a disorder involving disrupted brain structure and energy balance, not only plaque formation. They say a whole-brain, label-free approach could help surface changes that targeted assays might miss.

    While the findings are based on an animal model and would need validation in human tissue, the researchers suggest the approach could eventually inform earlier detection strategies and more region-specific treatment research. The study was published in ACS Applied Materials and Interfaces with support from U.S. federal research funders.

  • Waseda Researchers Apply Attachment Theory to Human-AI Bonds: What EHARS Reveals About Anxiety and Avoidance

    As AI chatbots and digital assistants become part of daily life, researchers are looking beyond trust and usefulness to understand the emotional side of human-AI interaction. A team at Waseda University argues that attachment theory, long used to explain human bonds, can also help explain why some people turn to AI for comfort and guidance.

    The researchers developed a new self-report measure called the Experiences in Human-AI Relationships Scale, or EHARS, to capture how users relate to AI in ways that resemble attachment patterns. The work, based on two pilot studies and a formal study, was published in Current Psychology in May 2025.

    Measuring attachment anxiety and avoidance

    EHARS focuses on two dimensions: attachment anxiety and attachment avoidance toward AI systems. Higher anxiety is linked to seeking reassurance and worrying that an AI will respond inadequately, while higher avoidance reflects discomfort with emotional closeness and a preference for distance.

    In the study, nearly 75% of participants reported turning to AI for advice, suggesting that many people already treat AI as a source of guidance. About 39% described AI as a constant, dependable presence, a finding the authors say is relevant to how emotional security can be sought through technology.

    What the findings do and do not mean

    The authors emphasize that the results do not prove people are forming genuine human-like attachments to AI. Instead, the study indicates that established psychological frameworks may help describe patterns in human-AI relationships as these tools become more conversational and socially responsive.

    That distinction matters because AI systems can simulate empathy without experiencing it, potentially shaping user expectations and dependency. Researchers and ethicists have increasingly warned that emotionally persuasive interfaces can heighten risks for vulnerable users, particularly in loneliness and mental health contexts.

    Implications for ethical AI design

    The team suggests EHARS could help designers and researchers evaluate how different users emotionally engage with AI, informing safer interaction patterns. In practice, that could mean more transparent disclosures, careful use of relational language, and guardrails to reduce overreliance where attachment anxiety appears high.

    As AI companions, coaching bots, and therapy-adjacent apps expand, measuring emotional dynamics may become as important as testing accuracy and security. The Waseda study positions attachment-informed evaluation as one tool for aligning AI behavior with user well-being and responsible product design.

  • New research shows when eye contact matters most, and why robots can read it too

    New research shows when eye contact matters most, and why robots can read it too

    For the first time, a new study has revealed how and when we make eye contact — not just the act itself — plays a crucial role in how we understand and respond to others, including robots.

    Led by cognitive neuroscientist Dr Nathan Caruana, researchers from the HAVIC Lab at Flinders University asked 137 participants to complete a block-building task with a virtual partner.

    They discovered that the most effective way to signal a request was through a specific gaze sequence: looking at an object, making eye contact, then looking back at the same object. This timing made people most likely to interpret the gaze as a call for help.

    Dr Caruana says that identifying these key patterns in eye contact offers new insights into how we process social cues in face-to-face interactions, paving the way for smarter, more human-centered technology.

    “We found that it’s not just how often someone looks at you, or if they look at you last in a sequence of eye movements but the context of their eye movements that makes that behavior appear communicative and relevant,” says Dr Caruana, from the College of Education, Psychology and Social Work.

    “And what’s fascinating is that people responded the same way whether the gaze behavior is observed from a human or a robot.

    “Our findings have helped to decode one of our most instinctive behaviors and how it can be used to build better connections whether you’re talking to a teammate, a robot, or someone who communicates differently.

    “It aligns with our earlier work showing that the human brain is broadly tuned to see and respond to social information and that humans are primed to effectively communicate and understand robots and virtual agents if they display the non-verbal gestures we are used to navigating in our everyday interactions with other people.”

    The authors say the research can directly inform how we build social robots and virtual assistants that are becoming ever more ubiquitous in our schools, workplaces and homes, while also having broader implications beyond tech.

    “Understanding how eye contact works could improve non-verbal communication training in high-pressure settings like sports, defense, and noisy workplaces,” says Dr Caruana.

    “It could also support people who rely heavily on visual cues, such as those who are hearing-impaired or autistic.”

    The team is now expanding the research to explore other factors that shape how we interpret gaze, such as the duration of eye contact, repeated looks, and our beliefs about who or what we are interacting with (human, AI, or computer-controlled).

    The HAVIC Lab is currently conducting several applied studies exploring how humans perceive and interact with social robots in various settings, including education and manufacturing.

    “These subtle signals are the building blocks of social connection,” says Dr Caruana.

    “By understanding them better, we can create technologies and training that help people connect more clearly and confidently.”

    The HAVIC Lab is affiliated with the Flinders Institute for Mental Health and Wellbeing and a founding partner of the Flinders Autism Research Initiative.

    Acknowledgements: Authors were supported by an Experimental Psychology Society small grant.

  • Study finds the brain processes speech in AI-like layers, offering new clues to how meaning is built

    Study finds the brain processes speech in AI-like layers, offering new clues to how meaning is built

    New research suggests the human brain may understand spoken language through a layered, step-by-step process that closely parallels how large language models handle text. By tracking neural activity as people listened to a continuous story, scientists found patterns that align with the progression from simpler to more complex representations seen in modern AI.

    The work, published in Nature Communications, analyzed high-temporal-resolution recordings from electrodes placed on the brain surface in clinical settings. Researchers compared the timing of neural responses with internal representations from well-known language models, including GPT-2 and Meta’s Llama 2.

    How meaning appears to unfold

    The team reports that early brain signals corresponded more closely to the earlier computational stages of AI systems that focus on basic word-level features. Later neural responses matched deeper model layers that integrate broader context, linking words into higher-level meaning.

    This alignment was especially pronounced in established language regions, including areas often associated with speech production and comprehension such as Broca’s area. In these regions, the strongest match tended to appear later in time, consistent with a gradual buildup of meaning.

    Rethinking classic language theories

    The findings add weight to the idea that comprehension is not driven primarily by rigid, rule-based structures applied instantly to each sentence. Instead, the results support a view in which the brain continuously updates interpretations as more context arrives, resembling statistical inference more than fixed symbolic parsing.

    Researchers also evaluated traditional linguistic descriptors, such as phoneme- and morpheme-level features, and found they explained real-time neural activity less effectively than the contextual features derived from AI models. That gap, the authors argue, suggests that context-rich representations may better capture how the brain tracks meaning in natural speech.

    A dataset meant to accelerate research

    Alongside the paper, the team released a public dataset designed to help other labs test competing theories of language processing against neural measurements. By pairing brain recordings with model-derived language features, the resource is intended to make comparisons across studies more consistent and reproducible.

    Experts caution that similarities do not mean the brain works the same way as today’s AI, which is trained on vast text corpora and built from artificial neural networks. Still, the results strengthen the case that AI language models can serve as useful scientific tools for probing how the brain constructs meaning over time.

  • New research explains why human language resists code-like efficiency and what it means for AI

    New research explains why human language resists code-like efficiency and what it means for AI

    Human language can seem inefficient compared with computer code, but researchers argue that its structure is optimized for the brain rather than for maximum compression. A new modeling study suggests people rely on familiar patterns to reduce mental effort during real-time conversation.

    The work, by linguist Michael Hahn and cognitive scientist Richard Futrell, was published in Nature Human Behaviour. Using information-theory-based modeling, they examined why languages worldwide tend to favor predictable word patterns instead of highly compact encodings.

    Efficiency for brains, not bits

    In principle, the same message could be transmitted with fewer symbols, similar to how computers use binary strings. The researchers contend that such a system would be harder for humans to learn and process because it would not align with how people store knowledge and anticipate meaning.

    Natural language, they argue, is tightly linked to shared experience, letting listeners map words onto familiar concepts quickly. That connection helps speakers avoid creating arbitrary, maximally compressed labels that would be information-dense but difficult to interpret.

    Predictability lowers cognitive load

    The model emphasizes that comprehension is incremental: listeners use each word to narrow down likely meanings before a sentence ends. This predictive processing makes everyday communication feel almost automatic, even if it is not mathematically optimal in terms of compression.

    As an illustration, the authors point to how grammatical order guides expectations in languages such as German. When familiar cues arrive in the expected sequence, the brain can prune unlikely interpretations early, whereas scrambled word orders force more effortful processing.

    What the findings suggest for AI

    The researchers say the results help explain why languages converge on structures that are learnable and robust under noisy, fast conditions like speech. Rather than chasing minimal code length, languages appear to balance expressiveness with the constraints of memory, attention, and prediction.

    The same logic could inform how developers evaluate and design large language models, which already rely heavily on predicting likely next words. The study suggests that systems built to communicate smoothly with people may benefit from prioritizing human-friendly predictability over pure information compression.

  • „Google“ įjungė svarbų „Gemini“ atnaujinimą „Docs“: DI dabar vykdys jūsų taisykles

    „Google“ įjungė svarbų „Gemini“ atnaujinimą „Docs“: DI dabar vykdys jūsų taisykles

    „Google“ plečia „Gemini“ integraciją „Google Docs“ ir į dokumentų kūrimą atneša funkciją, kuri iki šiol buvo patogesnė naudojant „Gemini“ atskirai internete ar mobiliojoje programėlėje. Nuo šiol „Docs“ naudotojai gali pateikti DI asmenines instrukcijas, kad atsakymai ir sugeneruotas tekstas geriau atitiktų jų poreikius.

    Ši galimybė leidžia nurodyti, kokiu tonu rašyti, kaip struktūruoti santraukas ar ko vengti. Pavyzdžiui, galima paprašyti, kad DI visada pateiktų trumpą dokumento santrauką iš trijų punktų, arba kad visus tekstus rašytų profesionaliai, nenaudodamas šnekamosios kalbos.

    „Google“ taip pat stiprina kontekstą tarp „Workspace“ programų, kad „Gemini“ geriau suprastų dokumentuose ir susijusiuose failuose esančią informaciją. Kartu atnaujinami ir DI įrankiai, tokie kaip „Help me write“ bei „Help me create“, o rašymo nuoseklumui skirta parinktis „Match writing style“ padeda išlaikyti tą patį stilių skirtinguose dokumentuose.

    Praktikoje tai ypač aktualu tiems, kurie reguliariai rengia klientų ataskaitas, pasiūlymus ar vidines politikos gaires. Tokiais atvejais galima iš anksto nurodyti, kad DI naudotų formalų stilių, laikytųsi įmonės komunikacijos principų ir pabaigoje visada pateiktų aiškias išvadas.

    Instrukcijos įvedamos atidarius „Gemini“ šoninį skydelį „Google Docs“ aplinkoje. „Google“ nurodo, kad galima pateikti iki 1 000 individualių instrukcijų, o atskiro nustatymo, kurį reikėtų įjungti, šiai funkcijai nėra.

    Atnaujinimas pradėtas diegti 2026 metų gegužės 4 dieną, o dėl laipsniško platinimo visus naudotojus turėtų pasiekti per 15 dienų. Funkcija numatyta „Business“ ir „Enterprise“ klientams, taip pat „Google AI Plus“, „Google AI Pro“ ir „Google AI Ultra“ prenumeratoriams.

    Kol kas ši galimybė diegiama tik „Google Docs“ ir nepaskelbta, kad tokiu pat būdu ji būtų įjungiama kitose „Workspace“ programose. Vis dėlto kryptis aiški: „Google“ siekia, kad DI pagalba dokumentuose būtų ne vien momentinė, bet ir nuosekliai prisitaikanti prie žmogaus bei organizacijos taisyklių.

  • „OpenAI“ DI telefonas gali sudrebinti rinką: kalbama apie 30 mln. vienetų iki 2028 metų

    „OpenAI“ DI telefonas gali sudrebinti rinką: kalbama apie 30 mln. vienetų iki 2028 metų

    „OpenAI“ svarsto ambicingą žingsnį į išmaniųjų telefonų rinką: analitiko Ming-Chi Kuo vertinimu, bendrovės kuriamas DI telefonas galėtų pasiekti apie 30 mln. pristatytų įrenginių 2027–2028 metais. Tai būtų panašus mastas, kokį per pirmuosius metus pasiekia kai kurios didžiosios „Samsung Galaxy“ serijos kartos.

    Pasak M.-C. Kuo, masinė gamyba galėtų startuoti 2027 metų pradžioje, nors anksčiau jis buvo minėjęs vėlesnį laiką. Tokia projekto sparta rodo, kad „OpenAI“ gali siekti kuo greičiau įsitvirtinti naujoje kategorijoje, kol DI funkcijos telefonuose dar dažnai lieka priedu, o ne pagrindine patirtimi.

    Idėja: telefonas be programėlių centro

    Tekste akcentuojama, kad „OpenAI“ telefono kryptis būtų vadinamoji agentinė sąveika, kai vartotojas užduoda tikslą, o sistema pati atlieka veiksmų grandinę. Tai reikštų bandymą nutolti nuo įprasto modelio, kuriame viskas sukasi aplink atskiras programėles ir jų pranešimus.

    Toks požiūris rinkoje laikomas vienu svarbiausių DI plėtros etapų, tačiau jis kelia ir praktinių klausimų: kaip užtikrinti patikimumą, privatumo kontrolę ir veiksmų skaidrumą, kai sistema pati inicijuoja užsakymus, rezervacijas ar kitus veiksmus. Būtent telefonas kaip nuolat su vartotoju esantis įrenginys dažnai įvardijamas kaip patogiausia platforma tokiai patirčiai.

    Planuojama nuosava sistema ir specialus lustas

    Anot M.-C. Kuo, „OpenAI“ gali kurti visiškai nuosavą įrenginį: ne tik aparatinę dalį, bet ir operacinę sistemą. Tai reikštų bandymą kontroliuoti visą grandinę nuo DI modelių veikimo iki energijos valdymo, kad DI funkcijos nekenktų našumui ir baterijos laikui.

    Taip pat minima, kad įrenginys galėtų naudoti pritaikytą „MediaTek“ „Dimensity 9600“ lustą. Tokie sprendimai dažnai pasirenkami siekiant geriau suderinti DI skaičiavimus įrenginyje, ryšio galimybes ir kainą, tačiau galutinis našumas ir energijos sąnaudos paaiškėja tik realiuose produktuose.

    Jony Ive vaidmuo lieka neaiškus

    Viešai žinoma, kad buvęs „Apple“ dizaino vadovas Jony Ive nuo 2025 metų vidurio yra prisiėmęs kūrybines ir dizaino atsakomybes „OpenAI“ projektuose. Vis dėlto kol kas nėra patvirtintos informacijos, ar jis tiesiogiai dirba prie telefono išvaizdos ir formos.

    Tuo pat metu rinkoje netyla spėlionės, kad „OpenAI“ galėtų siekti ne tik klasikinio telefono, bet ir alternatyvios formos įrenginio, pavyzdžiui, nešiojamo, mažiau į ekraną orientuoto sprendimo. Pastaraisiais metais bandymai su DI įrenginiais parodė, kad vien idėjos nepakanka: svarbiausia tampa praktinis patogumas ir reali nauda kasdienėse situacijose.

    Kol kas tai tebėra analitiko vertinimai ir rinkos lūkesčiai, o ne oficialiai patvirtintas produktas. Tačiau jei „OpenAI“ iš tiesų pasirinktų telefoną kaip DI centro platformą ir projektą įgyvendintų iki 2027 metų, tai galėtų tapti vienu didžiausių pokyčių išmaniųjų įrenginių rinkoje per pastarąjį dešimtmetį.

  • DI teisinėms paslaugoms: „Moritz“ po „Y Combinator“ pritraukė 9 mln. eurų ir žada greitesnes sutartis

    DI teisinėms paslaugoms: „Moritz“ po „Y Combinator“ pritraukė 9 mln. eurų ir žada greitesnes sutartis

    Teisinių technologijų startuolis „Moritz“ po dalyvavimo „Y Combinator“ pritraukė 9 mln. eurų investiciją. Finansavimo raundas, kurio paklausa viršijo siūlomą apimtį, sulaukė rizikos kapitalo fondų ir grupės vienaragių įkūrėjų palaikymo.

    Įmonę įkūrė Pamir Ehsas ir Stefan Mandaric, o jos tikslas – mažinti neefektyvumą, kuris dažnai siejamas su tradicinėmis teisinėmis paslaugomis. „Moritz“ teigia siekianti greitesnio proceso, aiškesnės kainodaros ir labiau prognozuojamų terminų.

    Kaip veikia „Moritz“ modelis

    „Moritz“ jungia DI ir patyrusių teisininkų tinklą, kad komercinės teisės užklausos būtų apdorojamos efektyviau. Klientai pateikia užduotį platformoje, o automatizuoti procesai atlieka didelę darbo dalį, kuri vėliau patikrinama ir užbaigiama kvalifikuotų teisininkų.

    Tokiu būdu bendrovė siekia sutrumpinti sutarčių ir kitų dokumentų parengimo laiką, kartu išlaikant teisinę atsakomybę. Taip pat akcentuojama skaidresnė kainodara, lyginant su įprastu valandiniu apmokestinimu.

    „Mūsų DI atlieka didžiąją darbo dalį, o teisininkai peržiūri ir galutinai patvirtina rezultatą. Klientai gauna teisinę atsakomybę, greitą įgyvendinimą ir skaidrią kainodarą“, – sakė Pamir Ehsas.

    Rezultatai ir plėtros planai

    Nuo veiklos pradžios „Moritz“ skelbia padėjusi daugiau nei 100 įmonių užbaigti susitarimus, kurių bendra sutarčių vertė viršija maždaug 1 850 000 000 eurų. Bendrovė nurodo veikusi Europoje, JAV ir Australijoje.

    Gautas lėšas startuolis planuoja skirti platformos tobulinimui, teisininkų tinklo plėtrai ir veiklos mastelio didinimui pagrindinėse rinkose. Ilgainiui „Moritz“ taip pat svarsto plėsti paslaugų spektrą už komercinės teisės ribų, pritaikant tą patį modelį platesniems teisiniams atvejams.