Artificial intelligence (AI) has revolutionized many technology areas. As a few examples, it has already been instrumental in improving and enabling voice recognition algorithms, digital assistants, advertisement recommendation engines and financial trading applications.[1] Significant investment is being made for further development of this promising new technology, with R&D spending on AI predicted to reach $57.6 billion by the end of 2021.[2] Along with these R&D efforts, companies are also trying to protect and monetize their AI inventions, in some cases opting to seek patent protection. From 2002 to 2018, the number of AI patent applications filed with the United States Patent and Trademark Office (USPTO) more than doubled, from 30,000 to 60,000.[3] These R&D efforts are no longer limited to software companies.

Artificial intelligence (AI) is rapidly permeating the enterprise security ecosystem, bringing a wide range of advanced capabilities to what is quickly becoming a key aspect of a successful digital business model. But while it might be tempting to just throw AI at the digital wall to see where it sticks, wiser business leaders are taking the time to ascertain where it can be used most effectively and how it must blend with normal operations so as not to hamper overall performance. According to Statista, the most popular security applications for AI involve protecting the network, endpoints and the data itself. Upwards of 75 percent of IT executives surveyed in 2019 report networking as the top area of concern, followed by 71 percent for data security and 68 percent for endpoints. Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.

Recent studies have shown that Deep Leaning models are susceptible to adversarial examples, which are data, in general images, intentionally modified to fool a machine learning classifier. In this paper, we present a multi-objective nested evolutionary algorithm to generate universal unrestricted adversarial examples in a black-box scenario. The unrestricted attacks are performed through the application of well-known image filters that are available in several image processing libraries, modern cameras, and mobile applications. The multi-objective optimization takes into account not only the attack success rate but also the detection rate. Experimental results showed that this approach is able to create a sequence of filters capable of generating very effective and undetectable attacks.

Due to limited computational cost and energy consumption, most neural network models deployed in mobile devices are tiny. However, tiny neural networks are commonly very vulnerable to attacks. Current research has proved that larger model size can improve robustness, but little research focuses on how to enhance the robustness of tiny neural networks. Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources. To this end, we propose a multi-objective oneshot network architecture search (NAS) algorithm to obtain the best trade-off networks in terms of the adversarial accuracy, the clean accuracy and the model size. Specifically, we design a novel search space based on new tiny blocks and channels to balance model size and adversarial performance. Moreover, since the supernet significantly affects the performance of subnets in our NAS algorithm, we reveal the insights into how the supernet helps to obtain the best subnet under white-box adversarial attacks. Concretely, we explore a new adversarial training paradigm by analyzing the adversarial transferability, the width of the supernet and the difference between training the subnets from scratch and fine-tuning. Finally, we make a statistical analysis for the layer-wise combination of certain blocks and channels on the first non-dominated front, which can serve as a guideline to design tiny neural network architectures for the resilience of adversarial perturbations.

It does design and manufacture chips – Huawei designs the chips for its 5G equipment and smartphones – but can't produce the advanced chips that are central to technologies like artificial intelligence, machine learning and the Internet of Things. Nevertheless its open ambition, supported by billions of dollars of state funding, is to become the major player in the sector as part of its broader ambition to dominate the building blocks of key 21st Century technologies. The US Congress has authorised subsidies for companies that invest in domestic chip research and manufacturing but not appropriated the funding for what would be a massively expensive program – semiconductor production is arguably the most sophisticated and challenging manufacturing process ever developed. The current chip shortage has highlighted America's vulnerability to external shocks but the transformation in the relationship with – and US perceptions of – China during the Trump presidency is a key motivator of the push for reshoring.

The UK's GCHQ has revealed how AI is set be used to boost national security. The UK's top intelligence and security body, GCHQ, is betting big on artificial intelligence: the organization has revealed how it wants to use AI to boost national security. In a new paper titled "Pioneering a New National Security," GCHQ's analysts went to lengths to explain why AI holds the key to better protection of the nation. The volumes of data that the organization deals with, argued GCHQ, places security agencies and law enforcement bodies under huge pressure; AI could ease that burden, improving not only the speed, but also the quality of experts' decision making. "AI, like so many technologies, offers great promise for society, prosperity and security. It's impact on GCHQ is equally profound," said Jeremy Fleming, the director of GCHQ.

The UK's Scientific Advisory Group for Emergencies (SAGE) advised the government to introduce mandatory hotel quarantine for travellers arriving into the UK two weeks ago, according to minutes from a meeting on 21 January that were leaked to the Times. On Thursday 21 January, SAGE reportedly warned that "reactive, geographically targeted" travel bans couldn't be relied on to prevent faster-spreading coronavirus variants, such as those identified in South Africa and Brazil, from reaching the UK, adding that: "no intervention, other than a complete, pre-emptive closure of borders, or the mandatory quarantine of all visitors upon arrival in designated facilities, irrespective of testing history, can get close to fully preventing the importation of new cases or new variants." A Downing Street spokesperson said SAGE did not directly advise UK prime minister Boris Johnson to close borders. Universities minister Michelle Donelan told Sky News that the government "always based our decisions on the best medical and scientific advice" and said "the SAGE advice actually said it would probably be ineffective, in fact, to close the borders, which was the same advice that we got at the time from the World Health Organization". Johnson announced geographically targeted hotel quarantine measures for travellers returning from 30 countries, including Brazil and South Africa, last week. UK health minister Matt Hancock urged people living in postcodes in England singled out for enhanced coronavirus testing for the so-called South Africa variant to stay at home unless "absolutely essential". Urgent door-to-door testing for the faster-spreading variant has been deployed after 11 cases with no link to foreign travel were identified in parts of England.

What if a new treatment for Alzheimer's disease exists today among existing U.S. Food and Drug Administration (FDA) approved drugs? A new peer-reviewed study published last week in Nature Communications by researchers at Harvard Medical School and Massachusetts General Hospital shows how an AI machine learning framework combined with genomics can help predict drug repurposing candidates for Alzheimer's disease. There are an estimated 50 million people living with Alzheimer's disease, a neurodegenerative disorder, and other forms of dementia globally according to the World Alzheimer Report 2018. In the United States, 5.8 million people are affected by Alzheimer's disease--two-thirds of whom are women. There are over 16 million people in the U.S. caring for those with Alzheimer's according to an article published today in Time by Maria Shriver, founder of the Women's Alzheimer's Movement, and George Vradenburg, co-founder of UsAgainstAlzheimer's.

Though artificial intelligence is changing how businesses work, there are concerns about how it may influence our lives. This is not just an academic or a societal concern but a reputational risk for companies, no company wants to be marred with data or AI ethics scandals that impacted companies like Amazon. For example, there was significant backlash due to the sale of Rekognition to law enforcement. This was followed by Amazon's decision to stop providing this technology to law enforcement for a year since they anticipate the proper legal framework to be in place by then. Al algorithms and training data may contain biases as humans do since those are also generated by humans.

The Bulgarian government has adopted a "Concept for the Development of Artificial Intelligence", planned until 2030. This strategy is in line with the documents of the European Commission, considering AI as one of the main drivers of digital transformation in Europe and a significant factor in ensuring the competitiveness of the European economy and high quality of life. Specific aspects of the European vision of "reliable AI" are included, namely that technological progress is accompanied by a legal and ethical framework to ensure the security and rights of citizens. The strategy also includes details on collecting accessible high-quality data, disseminating information and equal access to the benefits of AI technologies. In the concept document, an overview is given of the three main sectors involved in AI – sectors developing AI, sectors consuming AI, and sectors enabling the development and implementation of AI.