In computer science, a goal node is a node in a graph that meets defined criteria for success or termination. Heuristical artificial intelligence algorithms, like A and B, attempt to reach such nodes in optimal time by defining the distance to the goal node. When the goal node is reached, A defines the distance to the goal node as 0 and all other nodes' distances as positive values.
Confidential computing
Confidential computing is a security and privacy-enhancing computational technique focused on protecting data in use. Confidential computing can be used in conjunction with storage and network encryption, which protect data at rest and data in transit respectively. It is designed to address software, protocol, cryptographic, and basic physical and supply-chain attacks, although some critics have demonstrated architectural and side-channel attacks effective against the technology. The technology protects data in use by performing computations in a hardware-based trusted execution environment (TEE). Confidential data is released to the TEE only once it is assessed to be trustworthy. Different types of confidential computing define the level of data isolation used, whether virtual machine, application, or function, and the technology can be deployed in on-premise data centers, edge locations, or the public cloud. It is often compared with other privacy-enhancing computational techniques such as fully homomorphic encryption, secure multi-party computation, and Trusted Computing. Confidential computing is promoted by the Confidential Computing Consortium (CCC) industry group, whose membership includes major providers of the technology. == Properties == Trusted execution environments (TEEs) "prevent unauthorized access or modification of applications and data while they are in use, thereby increasing the security level of organizations that manage sensitive and regulated data". Trusted execution environments can be instantiated on a computer's processing components such as a central processing unit (CPU) or a graphics processing unit (GPU). In their various implementations, TEEs can provide different levels of isolation including virtual machine, individual application, or compute functions. Typically, data in use in a computer's compute components and memory exists in a decrypted state and can be vulnerable to examination or tampering by unauthorized software or administrators. According to the CCC, confidential computing protects data in use through a minimum of three properties: Data confidentiality: "Unauthorized entities cannot view data while it is in use within the TEE". Data integrity: "Unauthorized entities cannot add, remove, or alter data while it is in use within the TEE". Code integrity: "Unauthorized entities cannot add, remove, or alter code executing in the TEE". In addition to trusted execution environments, remote cryptographic attestation is an essential part of confidential computing. The attestation process assesses the trustworthiness of a system and helps ensure that confidential data is released to a TEE only after it presents verifiable evidence that it is genuine and operating with an acceptable security posture. It allows the verifying party to assess the trustworthiness of a confidential computing environment through an "authentic, accurate, and timely report about the software and data state" of that environment. "Hardware-based attestation schemes rely on a trusted hardware component and associated firmware to execute attestation routines in a secure environment". Without attestation, a compromised system could deceive others into trusting it, claim it is running certain software in a TEE, and potentially compromise the confidentiality or integrity of the data being processed or the integrity of the trusted code. == Technical approaches == Technical approaches to confidential computing may vary in which software, infrastructure and administrator elements are allowed to access confidential data. The "trust boundary," which circumscribes a trusted computing base (TCB), defines which elements have the potential to access confidential data, whether they are acting benignly or maliciously. Confidential computing implementations enforce the defined trust boundary at a specific level of data isolation. The three main types of confidential computing are: Virtual machine isolation Application isolation, also known as process isolation Function isolation, also known as library isolation Virtual machine isolation removes the elements controlled by the computer infrastructure or cloud provider, but allows potential data access by elements inside a virtual machine running on the infrastructure. Application or process isolation permits data access only by authorized software applications or processes. Function or library isolation is designed to permit data access only by authorized subroutines or modules within a larger application, blocking access by any other system element, including unauthorized code in the larger application. == Threat model == As confidential computing is concerned with the protection of data in use, only certain threat models can be addressed by this technique. Other types of attacks are better addressed by other privacy-enhancing technologies. === In scope === The following threat vectors are generally considered in scope for confidential computing: Software attacks: including attacks on the host’s software and firmware. This may include the operating system, hypervisor, BIOS, other software and workloads. Protocol attacks: including "attacks on protocols associated with attestation as well as workload and data transport". This includes vulnerabilities in the "provisioning or placement of the workload" or data that could cause a compromise. Cryptographic attacks: including "vulnerabilities found in ciphers and algorithms due to a number of factors, including mathematical breakthroughs, availability of computing power and new computing approaches such as quantum computing". The CCC notes several caveats in this threat vector, including relative difficulty of upgrading cryptographic algorithms in hardware and recommendations that software and firmware be kept up-to-date. A multi-faceted, defense-in-depth strategy is recommended as a best practice. Basic physical attacks: including cold boot attacks, bus and cache snooping and plugging attack devices into an existing port, such as a PCI Express slot or USB port. Basic upstream supply-chain attacks: including attacks that would compromise TEEs through changes such as added debugging ports. The degree and mechanism of protection against these threats varies with specific confidential computing implementations. === Out of scope === Threats generally defined as out of scope for confidential computing include: Sophisticated physical attacks: including physical attacks that "require long-term and/or invasive access to hardware" such as chip scraping techniques and electron microscope probes. Upstream hardware supply-chain attacks: including attacks on the CPU manufacturing process, CPU supply chain in key injection/generation during manufacture. Attacks on components of a host system that are not directly providing the capabilities of the trusted execution environment are also generally out-of-scope. Availability attacks: confidential computing is designed to protect the confidentiality and integrity of protected data and code. It does not address availability attacks such as Denial of Service or Distributed Denial of Service attacks. == Use cases == Confidential computing can be deployed in the public cloud, on-premise data centers, or distributed "edge" locations, including network nodes, branch offices, industrial systems and others. === Data privacy and security === Confidential computing protects the confidentiality and integrity of data and code from the infrastructure provider, unauthorized or malicious software and system administrators, and other cloud tenants, which may be a concern for organizations seeking control over sensitive or regulated data. The additional security capabilities offered by confidential computing can help accelerate the transition of more sensitive workloads to the cloud or edge locations. === Multi-party analytics === Confidential computing can enable multiple parties to engage in joint analysis using confidential or regulated data inside a TEE while preserving privacy and regulatory compliance. In this case, all parties benefit from the shared analysis, but no party's sensitive data or confidential code is exposed to the other parties or system host. Examples include multiple healthcare organizations contributing data to medical research, or multiple banks collaborating to identify financial fraud or money laundering. Oxford University researchers proposed the alternative paradigm called "Confidential Remote Computing" (CRC), which supports confidential operations in Trusted Execution Environments across endpoint computers considering multiple stakeholders as mutually distrustful data, algorithm and hardware providers. === Confidential generative AI === Confidential computing technologies can be applied to various stages of a generative AI deployments to help increase data or model privacy, security, and regulatory compliance. TEEs and remote attestation can protect the integrity of data during AI model training, keep
India AI Impact Summit 2026
The India AI Impact Summit 2026 (also abbreviated as the AI Impact Summit) was an international summit on artificial intelligence held at Bharat Mandapam, New Delhi, India, from 16 to 21 February 2026. It is the fourth in a series of global AI summits following the Bletchley Park AI Safety Summit in 2023, the AI Seoul Summit in 2024, and the AI Action Summit in Paris in 2025. Organised under the IndiaAI Mission by the Ministry of Electronics and Information Technology, it is the first summit in the series to be hosted by a Global South nation. This series of AI summits will continue with the AI Summit in Geneva to be hosted by Switzerland in 2027. The summit was inaugurated by Prime Minister Narendra Modi on 19 February 2026. The opening ceremony was also addressed by French President Emmanuel Macron and United Nations Secretary-General António Guterres. The summit was attended by over 20 heads of state and a delegation of global technology leaders including Sundar Pichai (Google), Sam Altman (OpenAI), and Demis Hassabis (DeepMind). The event faced criticism for organisational issues, misrepresentation of non-Indian products as Indian, and a perceived focus on trade fair activities over substantive governance. == Background == The AI Impact Summit was an international summit on artificial intelligence (AI) held in New Delhi from 16 to 20 February 2026. It followed the AI Action Summit in Paris in February 2025, the AI Seoul Summit in 2024 and the Bletchley Park AI Safety Summit in 2023. According to Crowell & Moring, the changing summit titles seemed to reflect a broader shift in focus away from AI safety and governance toward practical impact, implementation, and measurable outcomes. Ahead of the summit, an international panel of experts published the second International AI Safety Report. The summit was structured around three foundational pillars, termed "Sutras": People, Planet, and Progress. Seven thematic working groups were established to deliver outcomes across these pillars, covering AI for economic growth and social good; democratising AI resources; inclusion for social empowerment; safe and trusted AI; human capital; science; and resilience, innovation, and efficiency. == Programme == The summit ran over five days, later extended to six following overwhelming public response. Originally scheduled to conclude on 20 February, the event was extended to 21 February with expanded evening hours for the exhibition. === India AI Impact Expo === The India AI Impact Expo, inaugurated by Prime Minister Modi on 16 February, featured over 300 exhibitors from 30 countries across more than 10 thematic pavilions. Pavilions were organised across thematic zones aligned with the summit's three pillars, showcasing AI applications in healthcare, agriculture, education, and sustainable industry. === Leaders' Plenary and CEO Roundtable === The Leaders' Plenary on 19 February brought together heads of state, ministers, and representatives from multilateral institutions to outline national and global priorities on AI governance, infrastructure, and international cooperation. A CEO Roundtable, held the same evening, convened senior executives from global technology and industry firms with government leaders to discuss investment, research collaboration, and deployment of AI systems. === Research Symposium === A Research Symposium on AI and its Impact was held on 18 February, with the IIIT Hyderabad as knowledge partner. Discussions covered sovereign AI infrastructure, global adoption challenges, research breakthroughs, and policy priorities. == Participants == The summit drew delegations from over 100 countries, including more than 20 heads of state and 60 ministers. Notable attendees from the technology industry included Sundar Pichai (Google), Sam Altman (OpenAI), Dario Amodei (Anthropic), Demis Hassabis (Google DeepMind), and Mukesh Ambani (Reliance Industries). Representatives from multilateral institutions included Sangbu Kim of the World Bank. == Announcements and outcomes == === Indian AI models === Several Indian AI models and products were unveiled during the summit. Sarvam AI, an Indian AI laboratory, launched a new generation of large language models, including 30-billion and 105-billion parameter models using a mixture of experts architecture, as well as text-to-speech, speech-to-text, and vision models. Sarvam also introduced the Kaze smartglasses, described as the company's first hardware product, which Prime Minister Modi tested at the expo. The government-backed BharatGen Param2 model, a 17-billion parameter model supporting 22 Indian languages with multimodal capabilities, was also launched at the summit. === Infrastructure commitments === Union Minister Ashwini Vaishnaw outlined India's "whole-of-nation" AI strategy, describing plans to build a "frugal, sovereign and scalable" AI ecosystem. The government announced plans to add more than 20,000 GPUs to India's existing base of 38,000 under the IndiaAI Compute Portal. Microsoft announced at the summit that it was on track to invest US$50 billion by the end of the decade to bring AI to lower-income countries. Goa reaffirmed its commitment to artificial intelligence at the India AI Impact Summit 2026. === Guinness World Record === During the summit, India set a Guinness World Record for the most pledges received for an AI responsibility campaign in 24 hours, with 250,946 valid pledges collected between 16 and 17 February 2026. The campaign, conducted in partnership with Intel India as part of the IndiaAI Mission, exceeded its initial target of 5,000 pledges. == Controversies and criticisms == === Galgotias University incident === On 18 February, Galgotias University faced widespread criticism after a representative presented a robot dog at the university's exhibition pavilion as an indigenous development. Social media users identified the robot as the Unitree Go2, a commercially available product manufactured by Chinese company Unitree Robotics. IT Secretary S. Krishnan stated that the government did not want exhibitors to showcase items that were not their own, and the university was directed to vacate its stall. Galgotias University issued an apology, stating that the representative had been "ill-informed" and was not authorised to speak to the press. The incident drew political reactions, with the Indian National Congress using it to criticise the government. The controversy was amplified after Union IT Minister Ashwini Vaishnaw had earlier shared a video clip of the robot on social media, which was subsequently deleted. === Organisational issues === On day 1 of the Summit, Dhananjay Yadav, a Bengaluru-based entrepreneur had alleged that his product was stolen in the Summit. He called it as a pain for the people in an X post. He further wrote, "Think about this: We paid for flights, accommodation, logistics and even the booth. Only to see our wearables disappear inside a high-security zone". Later, the stolen devices were recovered by The Delhi Police. Bloomberg reported that delegates were left stranded without food or water during a security lockdown ahead of the Prime Minister's visit on 19 February. The summit venue was closed to the public on 19 February for the Prime Minister's visit, leading to criticism from attendees who had registered for that day. === Protests by the Indian Youth Congress (IYC) === On 20 February, some members of the Indian Youth Congress (IYC) carried out protests inside the venue with slogans such as "PM is compromised" and the criticism of the recent trade deal between India and the US. 4 of these members were sent to police custody by the court on 22 February. While Bharatiya Janta Party condemned these protests, with its spokesperson Shehzad Poonawalla saying, "From being anti-BJP, you have gone to being anti-national? If you have a problem with the BJP, then protest at the BJP office, Jantar Mantar, or outside the PM's office. But the people of the country and their alliance partners condemn them for their attempt to defame India in front of the entire world at the AI Summit." Congress leader Harish Rawat defended the protests, saying "it's also a fact that AI might become a tool in the hands of a few individuals… It's the opposition's job to warn against that… It's not the first time such international events have been opposed. I know how the BJP protested during the Commonwealth Games… To say that such opposition has happened for the first time is not correct. The BJP has been doing this while in the opposition." These protestors were granted bail by the Delhi high court on 2 March. == Reception and analysis == Bloomberg News reported that Prime Minister Modi used the summit to assert India's global AI ambitions following a challenging year in foreign policy. TechPolicy.Press published several critical analyses of the summit. One article argued that the summit's structure granted "multinational corporations parity with sovereign governments
Big Mechanism
Big Mechanism is a $45 million DARPA research program, begun in 2014, aimed at developing software that will read cancer research papers, integrate them into a cancer model and frame new hypotheses by the end of 2017 through the automated collection of big data and integrating across various disciplines such as knowledge-based NLP, curation and ontology, systems and mathematical biology by reading research abstracts and papers to extract pieces of causal mechanisms. == Ras gene == The program focuses on mutations in the Ras gene family, which underlie some one-third of human cancers. Currently, a rough road map shows interaction sequences among proteins affecting cell replication and death. However, the causal relations are poorly understood. == Plan == The program is to occur in three stages. The first is to read literature and convert it into formal representations. Second is to integrate the knowledge into computational models. Third is to produce experimentally testable explanations and predictions. Research teams are developing four separate systems targeting all three tasks. In February 2015, an evaluation meeting reviewed progress on the first stage. Multiple tasks were considered. One was extraction of experimental procedure details and evaluating statements such as "we demonstrate" and "we suggest." Another worked to map sentence meaning and relationships. The best machine-reading system extracted 40% of relevant information from a small corpus and correctly determined how each passage related to the model. The second stage is to become active in summer 2015, when members attempt to produce a single reference model. The third stage is the most challenging, because the artificial intelligence community has had limited success at developing hypothesis generators. Molecular biology may be more amenable, because most domain knowledge is technical and available in written form.
The Eye of Mexico
The Eye of Mexico (Spanish: El Ojo de México) is an outdoor sculpture in Mexico City. It is located in Ampliación Granada, Miguel Hidalgo, at the mixed-use development Neuchâtel Polanco, developed by the Canadian real estate company Ivanhoé Cambridge. The artwork was created by the Turkish artist Ferdi Alıcı and it was selected from among 350 proposals from artists from 35 countries. The project for The Eye of Mexico was developed by MIRA, a real estate investment and development company, and MASSIVart, a creative consulting agency. According to MIRA, upon its inauguration it became the first artwork in Latin America to use artificial intelligence (AI). The sculpture can read environmental and urban data using AI algorithms and transform the results into videos related to arts, science and technology. The ring was inaugurated on 20 May 2022 and it is 10 meters (33 ft) high and 3 meters (9.8 ft) wide.
Audio-visual speech recognition
Audio visual speech recognition (AVSR) is a technique that uses image processing capabilities in lip reading to aid speech recognition systems in recognizing indeterministic phones or giving preponderance among near probability decisions. Each system of lip reading and speech recognition works separately, then their results are mixed at the stage of feature fusion. As the name suggests, it has two parts. First one is the audio part and second one is the visual part. In audio part we use features like log mel spectrogram, mfcc etc. from the raw audio samples and we build a model to get feature vector out of it . For visual part generally we use some variant of convolutional neural network to compress the image to a feature vector after that we concatenate these two vectors (audio and visual ) and try to predict the target object.
Coronavirus breathalyzer
A coronavirus breathalyzer is a diagnostic medical device enabling the user to test with 90% or greater accuracy the presence of severe acute respiratory syndrome coronavirus 2 in an exhaled breath. As of the first half of 2020, the idea of a practical coronavirus breathalyzer was concomitantly developed by unrelated research groups in Australia, Canada, Finland, Germany, Indonesia, Israel, Netherlands, Poland, Singapore, United Kingdom and the USA. == Australia == In Australia, GreyScan CEO Samantha Ollerton and Prof. Michael Breadmore of the University of Tasmania are basing a coronavirus breathalyzer on existing technology that is used around the world to detect explosives. Another invention published from ABC News; produced by Colin Hickey and Examin Holdings, have released information on a new breathalyzer called the "Queensland Breath test" claiming its function has 98% efficiency, equipped with a replaceable plastic nozzle for reusability (February 2022). a statement in claim by Bruce Thompson, a professor at Swinburne University of Technology, Although this products is reliable, due to insufficient funding, the product is inaccessible. == Canada == Canary Health Technologies, headquartered in Toronto with offices in Cleveland, Ohio, is developing a breathalyzer with disposable nanosensors using AI-powered cloud-based analysis. According to a press release, clinical trials began in India during November 2020. The stated goal is to develop an accurate, reasonably priced screening tool that can be used anywhere and deliver a result in less than a minute. The company postulates that analyzing volatile organic compounds in human breath could potentially detect diseases before the on-set of symptoms, earlier than currently available methods. Moreover, the cloud-based technology is designed to be used as a disease surveillance apparatus. == Finland == By the end of June 2020, Forum Virium Helsinki, in collaboration with Finnish software firm Deep Sensing Algorithms, funded by the Helsinki-Uusimaa Regional Council, announced that testing of their device had begun with a control group in Kazakhstan, with plans to expand to the Netherlands, the United States, South Africa, Brazil and Finland throughout the summer. The efficacy of the Forum Virium Helsinki / Deep Sensing Algorithms device hinges on its AI component. "We are engaged in innovative cooperation with corporations to solve the coronavirus crisis, and we will help firms to use the city as a development platform. We are utilizing artificial intelligence and digitalization," said Forum Virium Helsinki CEO Mika Malin. == Germany == In March 2020, the Singaporean company RAM Global conducted research in Germany in hopes of developing a one-minute breathalyzer test for SARS-CoV-2 based on terahertz time-domain spectroscopy. The company attempted to develop a disposable test kit for direct detection of COVID-19 virion particles in breath, saliva and swab samples. On 31 March, RAM Global completed an initial clinical study on live patients at University Hospital Saarland. In April, the company pursued a small unknown sample study in which hospital doctors provided unknown samples in order to test accuracy in differentiating positive and negative samples. == Indonesia == Since April 2020, a team of researchers from Gadjah Mada University (UGM) has been developing an electronic nose called GeNose C19. The GeNose C19 can be used as a rapid, non-invasive screening tool in less than two minutes. A profiling test was carried out at the Bhayangkara Hospital and the Covid Bambanglipuro Special Field Hospital in Yogyakarta. GeNose C19 consists of gas sensors and an artificial intelligence-based pattern recognition system. The diagnostic test was carried out with the cooperation of nine multi-center hospitals. In the end of December 2020, GeNose C19 received a distribution permit from Indonesia's Health Ministry. Initially, 100 units will be released and each device will be able to perform 120 tests per day. The test is estimated to cost 15,000–25,000 Indonesian rupiah ($1–$1.8) and would take three minutes for the test and another two minutes to yield a result. Researchers hope to manufacture up to 1,000 GeNose C19 units, increasing the country's testing capabilities by 120 thousand subjects per day. Moreover, they aim to manufacture 10,000 units by February 2021. == Israel == In Israel, it is at the photonics lab of Gabby Sarusi, professor at Ben-Gurion University of the Negev, that research is underway as of midsummer 2020. Separately from Sarusi's project, in July 2020, it was reported that Israeli start-up Nanoscent in cooperation with Sheba Medical Center had devised a breathalyzer that Magen David Adom (MDA) is seeking to incorporate into existing drive-thru testing stations located throughout the country. Questionable intellectual property of Gabby Sarusi regarding this project is now under discussion in the court in Israel. == The Netherlands == A breath test with the SpiroNose device, made by the Dutch company Breathomix, has been developed and tested in collaboration with the Leiden University Medical Center (LUMC), Franciscus Gasthuis & Vlietland and the GGD Amsterdam. The breath test has been validated as a pre-screening test for people who have no or mild symptoms of COVID-19. From April 2021, the device was operational in COVID-19 test drive-ins, conferences and events, i.e. Eurovision Song Contest 2021. Subjects must abstain from alcohol for eight hours prior to taking the breath test. The SpiroNose contains four sets of seven different sensors that can measure the mixture of volatile organic compounds (biomarkers) in the exhaled air. These VOCs provide a picture of a person's metabolism. This 'breath profile' is forwarded to an online analysis platform. Here the breath profile is compared with other breath profiles of people with and without a COVID-19 diagnosis and analysed by algorithms. Data-analysis involves advanced signal processing and statistics based on independent t-tests followed by linear discriminant and ROC analysis. The test result is known within minutes. The breath test has a sensitivity/specificity for SARS-CoV-2 infection of 100/78, >99/84, 98/82% in validation, replication and asymptomatic cohorts of patients. The breath test reliably detects who is not infected. Such a subject will receive a test result immediately. Other subjects must promptly conduct a subsequent test, for example a PCR test or LAMP test. The test results can be viewed by the client and are not automatically interfaced to other databases, i.e. for public health surveillance, source and contact tracing, vaccination programs. In July 2021, the ministry stopped the tests with the SpiroNose because, according to the GGD, the device gives unusable results in some cases. Breathomix indicates that this is the result of the way in which the SpiroNose is deployed. The SpiroNose is and remains a reliable instrument for lung diseases. The analysis platform is developed conform the requirements of the standard ISO 27001 (Information Security) and NEN 7510 (Information Security in Health Care). A CE marking has been requested. In the meantime, the Dutch minister has granted a CE marking exemption on 25 January 2021. The device may also be used to detect other diseases, e.g., asthma, COPD, lung cancer, interstitial lung diseases (ILD). == Poland == In February 2021, the President of Poland, Andrzej Duda, announced that ML System S. A., headquartered in Zaczernie, Poland, had successfully developed a means of analyzing a patient's breath to test for the presence of coronavirus. According to an anonymous press release, test subjects exhale into a device in order to determine the presence of the coronavirus. The procedure, similar to that of a police breathalyzer, is said to take less than ten seconds. Independent clinical trials were begun in April 2021. In the first half of May 2021, a brief text concerning partial results was published by ML System, stating that independent clinical trials were successful with specificity (97,15%) and accuracy/sensitivity (86,86%), for CT (Cycle Threshold) assumed at 25, which is in line with the guidelines set out by the World Health Organization. Moreover, ML System in partnership with Rzeszów–Jasionka Airport published a statement indicating their intention to test the device at the airport. Similar plans exist between the manufacturer and the Warsaw Chopin Airport. Two large networks of laboratories in Poland, "Diagnostyka" and "ALAB Laboratoria", have signed a letter of intent with ML System. In agreement with ALAB, the parties declared cooperation in the implementation of the product named "COVID DETECTOR" on the Polish, German and Ukrainian markets. In addition, the companies declared joint activities aimed at extending the diagnosis with the use of "COVID Detector" to include mutations of the SARS-CoV-2 virus, differentiate the stage of the disease and ot