Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Na\"ive Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 17.9$\%$ and 36.9$\%$ gains in accuracy over reconstructed and adversarial attacked images, respectively.

Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities. Multimodal modeling helps to explain the interdependence between heterogeneous data sources, discovers new insights that may not be available from a single modality, and improves decision-making. Recently, coupled matrix-tensor factorization has been introduced for multimodal data fusion to jointly estimate latent factors and identify complex interdependence among the latent factors. However, most of the prior work on coupled matrix-tensor factors focuses on unsupervised learning and there is little work on supervised learning using the jointly estimated latent factors. This paper considers the multimodal tensor data classification problem. A Coupled Support Tensor Machine (C-STM) built upon the latent factors jointly estimated from the Advanced Coupled Matrix Tensor Factorization (ACMTF) is proposed. C-STM combines individual and shared latent factors with multiple kernels and estimates a maximal-margin classifier for coupled matrix tensor data. The classification risk of C-STM is shown to converge to the optimal Bayes risk, making it a statistically consistent rule. C-STM is validated through simulation studies as well as a simultaneous EEG-fMRI analysis. The empirical evidence shows that C-STM can utilize information from multiple sources and provide a better classification performance than traditional single-mode classifiers.

Artificial Intelligence is the process of making use of computers and machines to mimic human perception, decision-making, and other processes to complete a task. Put in other words, AI is

Three hundred and sixty-four days after she lost her job as a co-lead of Google's ethical artificial intelligence (AI) team, Timnit Gebru is nestled into a couch at an Airbnb rental in Boston, about to embark on a new phase in her career. Google hired Gebru in 2018 to help ensure that its AI products did not perpetuate racism or other societal inequalities. In her role, Gebru hired prominent researchers of color, published several papers that highlighted biases and ethical risks, and spoke at conferences. She also began raising her voice internally about her experiences of racism and sexism at work. But it was one of her research papers that led to her departure. "I had so many issues at Google," Gebru tells TIME over a Zoom call.

There's no doubt that the data science industry has come along way just in the last ten years, but you might be surprised that there is still a lot of growth potential in existing companies today. Perhaps one big reason for that is that we consistently face a shortage of qualified individuals, but I think another reason is that non-practitioners don't really understand the value that data science and artificial intelligence can bring. They hear the words "AI" or "machine learning" and associate those to Hollywood stereotypes like HAL from 2001: A Space Odyssey or Skynet from the Terminator movies. Of course, data science practitioners recognize that those Hollywood AIs represent a fictionalized potential for Artificial General Intelligence (AGI), but there's a lot more to this space than a talking computer. From random forest classifiers working well with structured data to deep learning working with unstructured data like text or images, there are a lot of different ways a data scientist can bring value to the table.

Huawei Consumer Business Group (BG) has announced the launch of Huawei nova 9 in Morocco, the ideal smartphone for the younger generation. "Incorporating a very rich set of innovative features and adorning itself with cutting-edge design elements, the latest addition to the nova series benefits from the presence of a very powerful camera system and receives all-new endowments that create new possibilities for users," according to Huawei. The Huawei nova 9 has a very powerful photographic device, reinforced by the presence of RYYB (CFA) color filters and an XD fusion engine, Huawei points out. The hardware-software integrated camera solution allows users to capture simply amazing images and videos even when light is lacking, Huawei says, noting that photos and videos will always be ready to be shared on devices. The smartphone also receives a magnificent 120 Hz curved screen, in addition to a very powerful processor and a very long battery life rechargeable through the 66 W Huawei SuperCharge1 system.

Experts say the technology isn't replacing human workers anytime soon. But the latest steps show warehouse robots are evolving as the computer vision and software that guide them grow more sophisticated, allowing them to take on more tasks that have been largely done by people. Puma North America Inc., a division of Puma SE, is using several robotic arms to assemble orders of clothing and shoes at a distribution center in Torrance, Calif.; the company plans to install more robots at another site outside Indianapolis. The technology from Nimble Robotics Inc., whose customers include Best Buy Co. and Victoria's Secret & Co., uses a combination of cameras, grippers and artificial intelligence to pluck items from bins that another automated system delivers to workstations usually staffed by people. Remote operators are on hand to assist if the robot has trouble picking up an object.

In 2019, a team of researchers published a meta-review of studies claiming a person's emotion can be inferred from their facial movements. They concluded that there's no evidence emotional state can be predicted from expression – regardless of whether a human or technology is making the determination. "[Facial expressions] in question are not'fingerprints' or diagnostic displays that reliably and specifically signal particular emotional states regardless of context, person, and culture," the coauthors wrote. "It is not possible to confidently infer happiness from a smile, anger from a scowl, or sadness from a frown." Alan Cowen might disagree with this assertion.

Iran-backed Yemeni fighters launched drone strikes on the United Arab Emirates that caused explosions and a deadly fire outside the capital, Abu Dhabi, ratcheting up security risks in the major oil-exporting region at a critical time. One of the biggest attacks to date on UAE soil ignited a fire at Abu Dhabi's main international airport on Monday and set fuel tanker trucks ablaze in a nearby industrial area. It took place days after Yemen's Houthi fighters warned Abu Dhabi against intensifying its air campaign against them. Crude extended gains to the highest level in seven years on Tuesday after the assaults in the UAE, OPEC's third biggest oil producer. Iran's longtime support of the Houthis means the incidents could roil regional diplomatic efforts to ease frictions and separate talks to restore Tehran's 2015 nuclear deal with world powers.