A new robot developed by MIT in the US is being used to kill coronavirus in a 4,000-square-foot warehouse using ultraviolet light (UV) light. The autonomous machine uses a specific type of short-wavelength UV, known as UVC, to kill microorganisms and disrupt their DNA in a process known as'ultraviolet germicidal irradiation'. UVC is emitted from the bot's four vertical beams as it nips around warehouse aisles, killing 90 per cent of coronavirus particles in 30 minutes. Because UVC light is harmful to humans, the robot has to do its work alone and is sent to do its sanitising shift when human workers have clocked off. The robot can map an entire industrial facility – in this case the Great Boston Food Bank (GBFB), a US non-profit that provides hunger relief.

What makes us humans so good at making sense of visual data? That's a question that has preoccupied artificial intelligence and computer vision scientists for decades. Efforts at reproducing the capabilities of human vision have so far yielded results that are commendable but still leave much to be desired. Our current artificial intelligence algorithms can detect objects in images with remarkable accuracy, but only after they've seen many (thousands or maybe millions) examples and only if the new images are not too different from what they've seen before. There is a range of efforts aimed at solving the shallowness and brittleness of deep learning, the main AI algorithm used in computer vision today.

AI tool turns low-pixel faces into realistic images A photo editing tool designed by a programming team at Duke University in Durham, North Carolina, raises prospects for sharper, cleaner images in digital presentations and also promises hours of fun for older-video game fans who can now generate crystal clear faces for low-pixel characters who populated early products. But the tool also unexpectedly brought to the surface concerns about bias in the use of datasets in massive machine learning projects. PULSE, Photo Upsampling via Latent Space Exploration, was created by Duke researchers to create more realistic images from low-pixel source data. In their research paper distributed earlier this year, the team explained how their approach differed from earlier efforts to generate lifelike images from 8-bit imagery. "Instead of starting with the low resolution image and slowly adding detail, PULSE traverses the high-resolution natural image manifold, searching for images that downscale to the original low resolution image," the report stated.

Conversational AI is a form of Artificial Intelligence that allows people to communicate with applications, Websites, and devices in everyday, human-like natural language via voice, text, touch, or gesture input. Conversational AI allows a fast interaction between users and the application using their own words and terminology. According to a Mordor Intelligence report on Chatbot Market: Growth, Trends, and Forecast (2020 - 2025), the chatbot market was valued at $17.17 billion in 2019 and is projected to reach $102.29 billion by 2025, registering a CAGR of 34.75 percent over the forecast period 2020 - 2025. "Virtual assistants are increasing because of deep neural networks, machine learning, and other advancements in AI technologies," according to the report. Virtual assistants, such as chatbots and smart speakers, are used for various applications across several end-user industries, such as Retail, Banking, Financial Services, and Insurance (BFSI), Healthcare, Automotive, and others.

Technology can be generally defined as the things, which are formed by the application of conceptual and corporeal efforts in demand, to gain peacetime and resolve definite difficulties. It also provides instant results to fulfill our lifestyle requirements like communication, health and creates ease in our daily tasks. The demand of AI technology is increasing day by day and human beings are using AI technologies which are working on the machines, taking the abilities of the humans and their experiences to make a new platform programmed in such a way that may become helpful to lessen their hard work. The purpose of AI technology is not only to speed up our work but it also provides assured level of accuracy and precision. Best AI is an interdisciplinary science with multiple methodologies, but advancements in machine, deep learning and neural networks which are giving benefit in every part of the tech.

This project gives you a chance to participate in enhancing the new machine learning approaches for exploring mars and unveiling its secrets.

In July 2017, The State Council of China released the "New Generation Artificial Intelligence Development Plan," outlining China's strategy to build a US$150 billion Chinese AI industry in a few short years, and to become the leading nation in AI by the year 2030. Other nations followed suit quickly with national AI strategies of their own – with the US trailing behind by nearly two years before developing a semblance of an AI initiative. The proposed 2021 budget for the national security budget in the US is $740 billion – with a billions of dollars being earmarked for AI specifically (learn more: US Public Sector AI Opportunity Report). AI applications play a considerable role in the direction of technology development in many defense sectors, particularly in surveillance, intelligence gathering, reconnaissance, logistics, command and control, cyberspace, and information operations – but AI's relevance for national security is just as much in it's implications for the economy as it is for defense itself. This article is based on my presentation at the UNICRI / Shanghai Institutes for International Studies event Artificial Intelligence – Reshaping National Security – held in Shanghai. While I'm not able to embed my full slide deck from that presentation publicly, I am able to share some of the key ideas from my talk – with a focus on AI job loss and defense implications.

LLamasoft published the results of a global retail supply chain study, which revealed that 73% of retailers believe artificial intelligence (AI) and machine learning can add significant value to their demand forecasting processes. Meanwhile, over half say it will improve 8 other critical supply chain capabilities. The research also found that while 56% of overperforming retailers, also known as'retail winners', use technology to model contingency plans for severe supply chain interruptions, a mere 31% of retailers who are not overperforming do the same. Overall, 56% of retailers surveyed are struggling with the ability to respond to rapid shifts, and the lack of flexibility has cost them during the disruptions such as COVID-19, with many seeing a huge drop in revenue as a result. In addition, 73% of'retail winners' have the foresight and ability to monitor capacity, which allows them to prepare for sudden shifts in demand and supply, compared to 35% of'other' or'under-performing' retailers.

Even at this early stage of the game, machine learning holds much promise, and is being applied to incredibly diverse fields – autonomous driving, medical screening, and supply-chain management. In many of these fields, the application of the technology has been extremely successful, predicting consumer demand and the outbreak of pandemics much more reliably than human intelligence. However, there remain some problems with the basic way in which machine learning works. ML algorithms require huge amounts of data, and data processing capability, to provide reliable predictions. Even if these resources are available, the algorithms can fail.

Nissan Chief Executive Makoto Uchida told shareholders Monday he is giving up half his pay after the automaker sank into the red amid plunging sales and plant closures in Spain and Indonesia. Uchida apologized for the poor results and promised a recovery by 2023, driven by cost cuts and new models showcasing electric cars and automated-driving technology. "We will tackle these challenges without compromise," he said at a live-streamed meeting. "I promise to bring Nissan back on a growth track." All the world's automakers have been hurt by nose-diving sales caused by the coronavirus pandemic.