Disruptive technology is the technology that affects the normal operation of a market or an industry. Digital disruption entails established companies and start-ups alike enlisting new technologies in the fight to dislodge incumbents, protect entrenched positions, or to re-invent entire industries and business activities. And to remain disruptive in the market, it is really important to keep innovating. This is crucial because, innovations occur now and then in every industry, however, to be truly disruptive, and innovation must entirely transform a product or solution that historically was so complicated only a few could access it. On a minimum level, digital transformation enables an organization to address the needs of its customers more simply and directly. But through disruptive innovation, companies can offer a far better way to users of doing things that current incumbents simply cannot compete with. Artificial intelligence (AI), E-Commerce, cloud, social networking, Internet of Things, 5G, blockchain and other emerging technologies are being leveraged to blur the lines between industries, creating new business models and converging sectors. A company that disrupts its market is in a great position to take advantage of new opportunities. Sometimes offering something different can change the whole market for the better. Most of the top disruptive companies get this label by offering highly innovative products and services and here are 100 such top disruptive companies listed below. The company provides innovative, managed cloud services to help its customers succeed. With best-in-class service and technology, 403Tech protects companies against cybercrimes while enabling greater efficiency and productivity. Some of its popular services include desktop support, server support, wired and wireless networking, virus removal, data recovery, and backup and hosted cloud services. Aegeus Technologies aims to design and develop robotic technologies and solutions.

Artificial intelligence (AI) has crossed a threshold. "In the past five years, AI has made the leap from something that mostly happens in research labs or other highly controlled settings to something that's out in society affecting people's lives," says Michael Littman, chair of the One Hundred Year Study on Artificial Intelligence, hosted at Stanford. It's easy to see what he's talking about: The technology's impact can be seen introducing automation, driving efficiency gains and enhancing productivity, creating new jobs, and reducing risks associated with cyber-threats and fraud. During the pandemic, AI enabled more effective testing for Covid-19 and faster vaccine development, and helped manage grocery supply chains and tailor lessons for individual students affected by remote schooling. As AI expands into more and more facets of our lives, there is also more scrutiny on who's developing it.

From self-driving cars to intuitive automatic vacuum cleaners, AI has taken over every industry and government organization around the world. Artificial intelligence is an emerging focus area of policy development in India. Many governments have begun to implement AI across various small-scale pilots. But many are still limited to implementation and experimentation. If implemented effectively, AI tools can generate benefits for both private and public-sector organizations.

AI and big data analytics are allowing healthcare providers in the Middle East to make faster, more cost-effective diagnostics, but security concerns are top of mind. AI and big data analytics are allowing healthcare providers in the Middle East to make faster, more cost-effective diagnostics, according to a broad cross-section of healthcare professionals. Along with the increasing use of AI and big data, though, security concerns about data privacy are also growing. AI is one of the fastest growing segments of the global healthcare market today. According to Frost & Sullivan forecasts, it will reach US$6.6 billion by the end of this year.

In the last decade, the use of Machine Learning techniques in anomaly-based intrusion detection systems has seen much success. However, recent studies have shown that Machine learning in general and deep learning specifically are vulnerable to adversarial attacks where the attacker attempts to fool models by supplying deceptive input. Research in computer vision, where this vulnerability was first discovered, has shown that adversarial images designed to fool a specific model can deceive other machine learning models. In this paper, we investigate the transferability of adversarial network traffic against multiple machine learning-based intrusion detection systems. Furthermore, we analyze the robustness of the ensemble intrusion detection system, which is notorious for its better accuracy compared to a single model, against the transferability of adversarial attacks. Finally, we examine Detect & Reject as a defensive mechanism to limit the effect of the transferability property of adversarial network traffic against machine learning-based intrusion detection systems.

We propose Selective Multiple Power Iterations (SMPI), a new algorithm to address the important Tensor PCA problem that consists in recovering a spike $\bf{v_0}^{\otimes k}$ corrupted by a Gaussian noise tensor $\bf{Z} \in (\mathbb{R}^n)^{\otimes k}$ such that $\bf{T}=\sqrt{n} \beta \bf{v_0}^{\otimes k} + \bf{Z}$ where $\beta$ is the signal-to-noise ratio (SNR). SMPI consists in generating a polynomial number of random initializations, performing a polynomial number of symmetrized tensor power iterations on each initialization, then selecting the one that maximizes $\langle \bf{T}, \bf{v}^{\otimes k} \rangle$. Various numerical simulations for $k=3$ in the conventionally considered range $n \leq 1000$ show that the experimental performances of SMPI improve drastically upon existent algorithms and becomes comparable to the theoretical optimal recovery. We show that these unexpected performances are due to a powerful mechanism in which the noise plays a key role for the signal recovery and that takes place at low $\beta$. Furthermore, this mechanism results from five essential features of SMPI that distinguish it from previous algorithms based on power iteration. These remarkable results may have strong impact on both practical and theoretical applications of Tensor PCA. (i) We provide a variant of this algorithm to tackle low-rank CP tensor decomposition. These proposed algorithms also outperforms existent methods even on real data which shows a huge potential impact for practical applications. (ii) We present new theoretical insights on the behavior of SMPI and gradient descent methods for the optimization in high-dimensional non-convex landscapes that are present in various machine learning problems. (iii) We expect that these results may help the discussion concerning the existence of the conjectured statistical-algorithmic gap.

It was while fleeing the civil war in South Sudan that Lual Mayen's mother gave birth to him 28 years ago. She had four children in tow and was near to the border with Uganda, in a town called Aswa. The journey was difficult; Mayen's two sisters died on the way and he became sick. No one thought he would survive. "I can't imagine what she had to go through. There was no food, no water, nothing," says Mayen. "I remember she said she was not the only woman who gave birth on the way. Other women abandoned their children because they didn't want them to suffer. But my mother thought: "He is a gift for me, I have to keep him."' Mayen's mother made it to northern Uganda with her newborn son and reunited with her husband in a refugee camp that remained their home for the next 22 years. Mayen grew up there, and although life was a struggle, he was happy and grateful for what he had. There wasn't much to do but Mayen says he found creative ways to keep himself entertained. Then, one day he had the chance to play the video game Grand Theft Auto, which mostly revolves around driving and shooting. "While I was playing, this thought came into my mind," he remembers. "In South Sudan, most of the population is under 30.

What comes to mind when we imagine a cutting-edge, tech-savvy workplace? But recent advances in technology, especially Artificial Intelligence, have attracted them too. AI-based tools have the ability to parse an unlimited amount of data, recognise patterns and apply them to new information. This allows legislators to have a dialogue with large constituents, analyse diverse opinions, participate remotely in plenary and committee meetings, and reduce paperwork through digitisation. Where is India in this picture?

AI and big data analytics are allowing healthcare providers in the Middle East to make faster, more cost-effective diagnostics, according to a broad cross-section of healthcare professionals. Along with the increasing use of AI and big data, though, security concerns about data privacy are also growing. AI is one of the fastest growing segments of the global healthcare market today. According to Frost & Sullivan forecasts, it will reach US$6.6 billion by the end of this year. Such growth rates are possible thanks to the huge amounts of data generated by a wide variety of devices, which can be analysed and acted on.

Recently, non-autoregressive (NAT) models predict outputs in parallel, achieving substantial improvements in generation speed compared to autoregressive (AT) models. While performing worse on raw data, most NAT models are trained as student models on distilled data generated by AT teacher models, which is known as sequence-level Knowledge Distillation. An effective training strategy to improve the performance of AT models is Self-Distillation Mixup (SDM) Training, which pre-trains a model on raw data, generates distilled data by the pre-trained model itself and finally re-trains a model on the combination of raw data and distilled data. In this work, we aim to view SDM for NAT models, but find directly adopting SDM to NAT models gains no improvements in terms of translation quality. Through careful analysis, we observe the invalidation is correlated to Modeling Diversity and Confirmation Bias between the AT teacher model and the NAT student models. Based on these findings, we propose an enhanced strategy named SDMRT by adding two stages to classic SDM: one is Pre-Rerank on self-distilled data, the other is Fine-Tune on Filtered teacher-distilled data. Our results outperform baselines by 0.6 to 1.2 BLEU on multiple NAT models. As another bonus, for Iterative Refinement NAT models, our methods can outperform baselines within half iteration number, which means 2X acceleration.