As the recent rise in Covid-19 threatens once again to shutter advertising agencies, film studios, and similar media "factories" globally, a quiet, desperate shift is taking place in the creation of new media, brought about by increasingly sophisticated AI capabilities. A new spate of actors and models are making their way to people's screens, such as pink-haired Imma, right, who has developed an extensive following in Japan on Instagram and TikTok, and is appearing increasingly on the covers of Japanese magazines. Imma joins a growing host of digital avatars who are replacing human actors, models, and photographers with computer-generated equivalents. Cloud-based GPUs and sophisticated game and modeling software have increasingly attracted the attention of a new generation of artist/programmers who are taking advantage of this to generate images, video, and audio that are becoming increasingly indistinguishable from reality, especially when that reality is otherwise captured via jump cuts, and matte overlays that have made tools such as TikTok and Reels the primary tools for video production for the typical Instagram celebrity. The business potential for such virtual models and spokespeople is huge, according to a recent piece by Bloomberg on digital avatars. Such avatars have obvious benefits over their flesh and blood counterparts.
This is an article by Gabriel Leung, Dean of Medicine at Hong Kong University Medical Center and Malik Peiris Professor at the University Hong Kong 1) COVID vaccines are needed, even if they have minimal impact on transmission 2) COVID vaccines may not help us achieve herd immunity 3) COVID vaccine trials primarily assess prevention of virologically confirmed disease - not infection or transmission 4) an "effective" vaccine confers protection from disease but might not reduce spread 5) if COVID vaccines are effective in reducing morbidity & mortality in high-risk groups, they would have an important role, irrespective of impact on transmission and population immunity 6) if high-risk populations can be shielded by vaccination, COVID control measures could be recalibrated 7) the idea that COVID vaccine-induced population immunity will allow a return to normalcy may be based on false assumptions 8) no country will be truly safe until the entire world is vaccinated. This new study from Akiko Iwasaki, PhD and colleagues at Yale University offers the first clear evidence that COVID can invade brain cells 1) 40-60% of hospitalized COVID patients experience neurological complications including nerve damage and stroke 2) this study suggests that COVID in the brain may be more lethal than the respiratory infection caused by COVID 3) COVID hijacks brain cells to make copies of itself then exploits the brain cells' machinery to multiply 4) then COVID chokes off oxygen to adjacent brain cells causing them to die 5) a few days into the infection there is a dramatic decrease the number of synapses (the connections between neurons in the brain) 6) the researchers didn't find any evidence of an immune response to remedy this problem. It's a silent infection with evasion mechanisms 7) some people may be susceptible because of their genetic background or high viral load. Researchers used Summit Supercomputer to analyze 2.5 billion genetic combinations from COVID; then they made the Bradykinin Hypothesis 1) it took Summit 1 week to run the numbers. These high-powered microscopic images show very high viral loads of SARS-CoV-2 on human respiratory surfaces ready to spread the virus 1) Camille Ehre PhD and colleagues at UNC Chapel Hill School of Medicine generated these microscopic images showing very high viral loads of SARS-CoV-2.
The economic recession that follows as a consequence of the Covid-19 crisis and in particular the demise of certain sectors of the economy (physical retail, hospitality sector, etc) means that there will be greater pressure on politicians around the world to consider how to stimulate GPD growth in the post-pandemic world. However, there are also increasing pressures on politicians to combat the threat posed by Climate Change. Are the desired objectives of GDP and employment growth as well as reducing pollution at odds with each other? What if there is a pathway to GDP growth with the creation of new jobs and yet at the same time we are able to reduce emissions of Green House Gasses (GHGs)? A report entitled "How AI can enable a sustainable future" by PWC and commissioned by Microsoft (lead authors Celine Herweijer of PWC and Lucas Joppa of Microsoft) estimates that using AI for environmental applications across four sectors – agriculture, water, energy and transport. The report estimated that such applications could contribute up to $5.2 trillion USD to the global economy in 2030, a 4.4% increase relative to business as usual.
During the last COVID-19 process, it has been seen that artificial intelligence can provide advance information about upcoming epidemics that have not yet been seen worldwide. To this end, the BlueDot company warned, using artificial intelligence algorithms, to abstain about the Chinese city of Wuhan in December 2019. However, the World Health Organization was able to make a similar warning only in January 2020. "BlueDot's outbreak risk software safeguards lives by mitigating exposure to infectious diseases that threaten human health, security, and prosperity" https://bluedot.global/ Thanks to the exploratory analysis and the rapid processing of up-to-date data, companies like BlueDot continue to predict where other Asian city outbreaks may occur by analyzing travel routes and flight paths.
Based out of Singapore, Gero develops new drugs for ageing and other complicated disorders using its proprietary developed artificial intelligence (AI) platform. Recently, the company has secured $2.2 million (€1.9 million) in Series A funding, bringing the total capital raised since Gero's founding to over $7.5 million (€6.4 million). Gero's founder Peter Fedichev, said, "We are happy with the recognition and support from these strategic investors who themselves are acknowledged leaders in the fields of AI and biotechnology. This will help us attain the necessary knowledge at the junction of biological sciences and AI/ML technologies that is necessary for the radical acceleration of drug discovery battling the toughest medical challenges of the 21st century. We hope that the technology will soon lead to a meaningful healthspan extension and quality of life improvements " The round was led by Bulba Ventures with participation from previous investors and serial entrepreneurs in the fields of pharmaceuticals, IT, and AI.
The first question many people ask about artificial intelligence (AI) is, "Will it be good or bad?" The answer is … yes. Canadian company BlueDot used AI technology to detect the novel coronavirus outbreak in Wuhan, China, just hours after the first cases were diagnosed. Compiling data from local news reports, social media accounts and government documents, the infectious disease data analytics firm warned of the emerging crisis a week before the World Health Organization made any official announcement. While predictive algorithms could help us stave off pandemics or other global threats as well as manage many of our day-to-day challenges, AI's ultimate impact is impossible to predict.
Anomalies are occurrences in a dataset that are in some way unusual and do not fit the general patterns. The concept of the anomaly is generally ill-defined and perceived as vague and domain-dependent. Moreover, no comprehensive and concrete overviews of the different types of anomalies have hitherto been published. By means of an extensive literature review this study therefore offers the first theoretically principled and domain-independent typology of data anomalies, and presents a full overview of anomaly types and subtypes. To concretely define the concept of the anomaly and its different manifestations the typology employs four dimensions: data type, cardinality of relationship, data structure and data distribution. These fundamental and data-centric dimensions naturally yield 3 broad groups, 9 basic types and 61 subtypes of anomalies. The typology facilitates the evaluation of the functional capabilities of anomaly detection algorithms, contributes to explainable data science, and provides insights into relevant topics such as local versus global anomalies.
Researchers from Facebook and the French National Institute for Research in Digital Science and Technology (Inria) have developed a new technique for self-supervised training of convolutional networks used for image classification and other computer vision tasks. The proposed method surpasses supervised techniques on most transfer tasks and outperforms previous self-supervised approaches. "Our approach allows researchers to train efficient, high-performance image classification models with no annotations or metadata," the researchers write in a Facebook blog post. "More broadly, we believe that self-supervised learning is key to building more flexible and useful AI." Recent improvements in self-supervised training methods have established them as a serious alternative to traditional supervised training. Self-supervised approaches however are significantly slower to train compared to their supervised counterparts.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Apparel companies hit by the coronavirus pandemic have been tapping artificial intelligence technology to boost sagging sales, using it to predict the designs and colors that will come into trend. One provider of such technology is Tokyo-based startup Neural Pocket Inc., which runs a system that automatically collects big data related to clothing on various fashion websites, Instagram and other social networking services to analyze day-to-day changes in trends. The company predicts the next hit products by analyzing data on colors, designs and clothing lengths. Such an approach using AI technology is new in the apparel industry, which generally relies on the experience and instinct of designers to predict trends. But as the coronavirus pandemic takes its toll on retailers, demand is growing to efficiently predict the next hot items in fashion.