Fujitsu is not a parasite for continuing to profit from government contracts in the wake of the Post Office Horizon scandal, its boss told MPs. European chief executive Paul Patterson said Fujitsu had been given £500m of contract extensions despite its faulty software being at the centre of the huge miscarriage of justice. We are not a parasite, the government has got an option as to whether they wish to extend those contracts or not, he said, adding it would not bid for new business. Patterson also repeatedly refused to say how much Fujitsu would contribute to the £1.8bn redress scheme for victims of the scandal, currently funded by taxpayers. More than 900 sub-postmasters were prosecuted after the faulty Horizon computer system made it look like money was missing from their branch accounts.

Victims of the Horizon Post Office scandal could meet face-to-face with Fujitsu and Post Office representatives as part of a restorative justice effort. The charity overseeing a new scheme said the first five months were an initial pilot phase, but it hoped the scheme would last five years and include extra initiatives such as a special commemorative postage stamp. It comes on top of the various financial compensation schemes in place for sub-postmasters. The Horizon IT scandal saw hundreds of sub-postmasters falsely accused of embezzling Post Office funds after faulty software suggested money was missing from their branch accounts. Children affected by Post Office scandal'should get grants' More than 900 sub-postmasters were wrongly prosecuted because of incorrect information from the Horizon computer system.

Sumitomo Mitsui Banking is tapping Fujitsu's artificial intelligence models to bolster its advisory services for customers grappling with rising wages and materials costs. The banking arm of Sumitomo Mitsui Financial Group is in talks to provide corporate client data to Fujitsu to run through the IT company's multimodal machine learning tools to make business forecasts, according to people familiar with the matter. The AI-inferred demand predictions would help some of the bank's biggest customers make key decisions from staffing to procurement to capital spending and financing, the people said, asking not to be named discussing nonpublic information. The two companies will soon sign a basic agreement, they said. The move would be a rare instance of a Japanese bank allowing another company access to sensitive customer data, such as store-by-store visitor and sales numbers.

The most interesting TV I've watched recently did not come from a conventional television channel, nor even from Netflix, but from TV coverage of parliament. It was a recording of a meeting of the AI in weapons systems select committee of the House of Lords, which was set up to inquire into "how should autonomous weapons be developed, used and regulated". The particular session I was interested in was the one held on 20 April, during which the committee heard from four expert witnesses – Kenneth Payne, who is professor of strategy at King's College London; Keith Dear, director of artificial intelligence innovation at the computer company Fujitsu; James Black from the defence and security research group of Rand Europe; and Courtney Bowman, global director of privacy and civil liberties engineering at Palantir UK. An interesting mix, I thought – and so it turned out to be. Autonomous weapons systems are ones that can select and attack a target without human intervention. It is believed (and not just by their boosters) that these systems could revolutionise warfare, and may be faster, more accurate and more resilient than existing weapons systems.

It's difficult to visualise the true scale of AI, as it's almost certainly more than you imagine – it's going to contribute more to the global economy than the current GDP of India and China combined. PwC research suggests that AI could contribute as much as $15.7 trillion by 2030, and by singularly responsible for a 26 per cent boost in the GDP of local economies. That would place it as one of the most fundamentally transformational changes in human history and, PwC notes, there is the opportunity for emerging economies to leapfrog developed ones by being faster on the AI uptake. However, for all the promise of AI, there remain challenges. Gartner research suggest that only 54 per cent of AI projects make it from pilot to production. The challenge, Garter says, has to do with scale.

Japan-based technology major Fujitsu has announced its new research centre in India. The research centre located in Bengaluru aims to strengthen focus on research and development (R&D) into artificial intelligence (AI) and machine learning (ML) technologies, as well as quantum software. Fujitsu India, in a statement, said the number of its researchers will reach 50 by fiscal 2024. The Group will expand its research fields into security and other areas. It will also conduct software R&D in collaboration with Fujitsu's global network of research centres in areas including Japan, Europe, and the United States to create software for global distribution, the company said in a statement.

Researchers at Fujitsu and the MIT Center for Brains, Minds and Machines (CBMM) have achieved a "major milestone" in the quest to bolster the accuracy of AI models tasked with image recognition. As described in a new paper presented at NeurIPS 2021, the collaborators have developed a method of computation that mirrors the human brain to enable AI that can recognize information that does not exist in its training data (also called out-of-distribution data, or ODD). Although AI is already used for image recognition in a range of contexts (e.g. the analysis of medical x-rays), the performance of current models is highly sensitive to the environment. The significance of AI capable of recognizing ODD is that accuracy is maintained in imperfect conditions – for example, when the perspective or light level differs from the images on which the model was trained. MIT and Fujitsu achieved this feat by dividing deep neural networks (DNNs) into modules, each of which is responsible for recognizing a different attribute, such as shape or color, which is similar to the way the human brain processes visual information.

Researchers at Fujitsu and the MIT Center for Brains, Minds, and Machines (CBMM) have made a "crucial step" in enhancing AI model image recognition accuracy. The scientists, as reported in a new study presented at NeurIPS 2021, have developed an approach for computing that mimics the human brain to create AI capable of detecting information that isn't present in its training data (also known as out-of-distribution data or ODD). The nature of the data, as well as its size and characteristics, have a major impact on how AI technologies may be used. Despite their current application in a variety of domains (e.g., medical x-rays analysis), existing models are highly sensitive to context. The importance of AI capable of recognizing ODD is that accuracy is preserved in conditions that are not ideal, such as when the perspective or light level differs from those used to train the model.

Researchers at Fujitsu and the MIT Center for Brains, Minds and Machines (CBMM) have achieved a "major milestone" in the quest to bolster the accuracy of AI models tasked with image recognition. As described in a new paper presented at NeurIPS 2021, the collaborators have developed a method of computation that mirrors the human brain to enable AI that can recognize information that does not exist in its training data (also called out-of-distribution data, or ODD). Although AI is already used for image recognition in a range of contexts (e.g. the analysis of medical x-rays), the performance of current models is highly sensitive to the environment. The significance of AI capable of recognizing ODD is that accuracy is maintained in imperfect conditions - for example, when the perspective or light level differs from the images on which the model was trained. MIT and Fujitsu achieved this feat by dividing deep neural networks (DNNs) into modules, each of which is responsible for recognizing a different attribute, such as shape or color, which is similar to the way the human brain processes visual information.

Creating a programmable software infrastructure for telecommunication operations promises to reduce both the capital expenditure (CAPEX) and the operational expenses (OPEX) of the 5G telecommunications operators. What is exciting to many of us who work in this space is that the convergence of telecommunications, the cloud, and edge infrastructures will open up opportunities for new innovations and revenue for both the telecommunications industry and the cloud ecosystem. In this blog, we focus on video, the dominant traffic type on the internet since the introduction of 4G networks. With 5G, not only will the volume of video traffic increase, but there will also be many new solutions for industries, from retail to manufacturing to healthcare and forest monitoring, infusing deep learning and AI for video analytics scenarios. The symbiotic evolution of video analytics and edge computing provides opportunities for operators to offer new services which they can monetize with their customers.