The disruption caused by the coronavirus has highlighted, time and again, how our pre-pandemic obsession with ‘going automated’ across industries held water. But away from production lines and other blue-collar applications, the University of Liverpool (UoL), UK has shown how robots can carry huge benefits by working non-stop within the bounds of academia too.

Branded an “intelligent machine with social distancing skills,” the £100,000 (US$126,000) robotic researcher – essentially a remotely-operated ‘robo-chemist’ – is equipped with a level of artificial intelligence (AI) that goes beyond typical robotic machinery, allowing it to utilize previous results and, ultimately, ‘decide what to do next’.

The robotic arm has been lifting test tubes and siphoning serums day and night in the hunt for solar cell reaction catalysts, but researchers believe the robot – or at least the technology underpinning it – could also be repurposed for the fight against Covid-19 in vaccine development.

TechHQ previously highlighted the power of AI in making scientific in-roads faster, cheaper and more-effective, by rapidly crunching through vast banks of data that would take human researchers months, if not years. This sentiment is echoed by the robot’s developer Benjamin Burger: “[The robot] frees my time to focus on innovation and new solutions, rather than doing the same action.”

And that neatly sums up the core concept of Robotic Process Automation (RPA); software-based scripts that are being employed across multiple applications in businesses to expedite repetitive tasks and minimize human-error. But when it comes to physical robots, we’re more used to seeing them on production lines, or in places such as mines, quarries, or offshore wind farms where it can simply be too dangerous for humans to venture.

A new report by the Royal Society of Chemistry, however, emphasizes the importance of “urgently embracing” robotics, AI and advanced computing as part of a post-Covid national research strategy. Like the every other place of work, scientists have been rendered ‘remote’ by enforced lockdowns and safety concerns,  but robo-scientists can continue to carry out ground-breaking research in situ.

This isn’t a replacement strategy. Instead, robotics systems like that of UoL’s could do the heavy-lifting in laboratories – with oversight from a potentially global team – while researchers can shift their focus to other parts of the project or more valuable tasks.

Speaking to the BBC, Deirdre Black, head of research and innovation at the Royal Society of Chemistry, said by leveraging robotics and automation,  scientists can “explore bigger and more complex problems, like decarbonization, preventing and treating disease, and making our air cleaner.”

With every industry grappling with the implications of the pandemic for months, if not years to come, we can expect robotics and automation technologies to see further rapid adoption not just in heavy industries, but in the worlds of science and academia too which will be equally as important to recovery.

We have already seen examples of this shift with Pepper (Softbank Robotics’ programmable humanoid) lending a personable touch to Covid-19 patients in intensive care.

The post Robotics in research – freeing up time for socially-distanced innovation? appeared first on Artificial Intelligence.

See Spot walk. See Spot sit. See Spot roll over. See Spot run onto a Norwegian oil rig to sniff out lethal gas leaks!

Boston Dynamics’ next-generation robotic device, affectionately nicknamed Spot, will soon be embarking on a new test mission aboard an offshore oil rig for petroleum product producer Aker BP and AI software company Cognite. The newly announced project will be rolled out to test a number of advanced robots and drones on Aker BP’s Skarv installation in the Norwegian Sea later this year.

Aker BP is embarking on this plan to get a “leg up” on the oil industry’s digitalization process to make its operations safer and more effective by challenging Spot’s ability to perform autonomous inspections, monitor systems, detect hydrocarbon leaks, collect data, and self-generate maintenance reports.

“Our vision is to digitalize all our operations from cradle to grave in order to increase productivity, enhance quality, and improve the safety of our employees,” Aker BP’s CEO Karl Johnny Hersvik said in a press release. “Exploring the potential of robotics offshore underpin our digital journey.”

Created as a multi-function mobile platform that can be fully customized according to its clients or owners’ needs, Spot is a nimble quadruped robot equipped with a multi-eyed perception system that includes cameras and 3D sensors used to help navigate obstacles and survey rough terrain, allowing the intelligent machine to mount stairs and step over debris. Its sleek, sturdy legs are powered by 12 custom motors that let it trot along at a top speed of 1.6 meters per second.

“The key to Aker BP and Cognite’s robotics initiative is that it combines industry-leading hardware and software. By ingesting data collected by robots into Cognite Data Fusion, Aker BP engineers will be able to see it in context with data from across the company’s operations and make data-driven decisions that improve efficiency and safety,” said Dr. John Markus Lervik, CEO of Cognite.

Aside for the aforementioned tasks, Spot might also be required to carry out aerial and underwater inspections, provide immediate response to dangerous situations that go unnoticed by traditional automation, and generally complete hazardous work that takes humans safely out of harm’s way, all while providing onshore operators with remote telepresence capabilities on offshore installations.

“We’re excited to see innovative partners such as Cognite validating Spot’s ability to reduce risk to humans and provide value for the energy industry,” said Michael Perry, vice president of business development at Boston Dynamics.

Now if Spot can only be programmed to chase problematic cats off oil and gas platforms, that might be something to truly praise!

The post BOSTON DYNAMICS’ ROBOTIC DOG GETS A JOB WORKING AN OFFSHORE OIL RIG appeared first on Artificial Intelligence.

Data is everywhere; in the webpages you visit, in blog posts you read, on social media platforms you post on, the videos you watch – all of it and more. It would suffice to say that everything you do on the Internet creates data – as does everything you don’t. We generate nearly 2.5 quintillion bytes of information daily, while Google alone is estimated to store over 10 Exabytes of data on a daily basis.

Digital data from all the above sources and more contributes heavily to the massive influx of Big Data and is changing how businesses operate. The quantity and variety of data and the speed at which it is generated and processed each minute makes this phenomenon truly “big”. It is these variables that define Big Data and make it such a dominant factor in manufacturing, sales, marketing, operations, marketing research and business analysis to name a few of its applications. Big Data has led to significant developments in analytics of both text and video, detecting fraud, and predicting consumer trends. However, while solving several problems for both businesses and consumers, Big Data brings with it challenges of its own.

While methods of storing data and analyzing it have advanced considerably in the last 10 years, the fundamental systems based on Big Data have begun to create potential problems. As a result, businesses are left asking some pertinent questions that are relevant to the future of Big Data.

The volume of data out there is massive and what is needed is a system that can sift through it and make sense of it. While many Big Data systems are relatively new, they are becoming redundant or are overloaded, and are often incapable of responding to demands as they emerge and grow.

Currently, all processes relating to Big Data are executed and overseen by humans. However, imagine if complex processes like extricating relevant data and refining it could be delegated to an intelligent machine. Putting other Big Data processes on auto mode and delegating the machine to manage it could make business processes quicker and smarter. Sounds exciting? This day, however, might just arrive earlier than we think.

Big Data and AI almost seem like a match made in digital heaven. While Big Data can expertly sieve out useful information, AI can help businesses look for useful insights from this information. There are several ways AI can do this, such as through automation of business processes and self-learning and optimization of its performance.

However, its most important function is to act as a platform to facilitate interactions between humans and machines. This would be most evident in sales and marketing processes. In addition to a heavy emphasis on CRM and market research, AI and Big Data are set to revolutionize the role of the salesperson in the next few years. Their application of several horizontal technologies allows AI to easily integrate with any function, product, or service. Businesses that have adopted AI-enhanced tools have seen impressive results as to the effectiveness of sales and marketing activities.

Hence, the point to be noted is that Big Data and AI are technologies that are a synergistic match. They have proven their worth in several practical situations as well, generating high ROIs for their users. The stream of data is nowhere close to slowing down and the sheer volume of data to be utilized continues to grow. But eventually, it is the correlation between Big Data systems and AI that will drive critical decision making.

This next wave of the espousal of AI and Big Data and all the scope for innovation it will bring is an exciting proposition for data scientists and analysts. For businesses, it’s a highly promising development. But like any other new technology and process, it needs companies to be prepared, and grab the proverbial bull by the horns before it knocks you down. If current Big Data systems are finding it challenging to adapt to the rising volume and velocity of Big Data today, then it’s time to re-assess their relevance and root out outdated, inefficient systems.

The post Artificial Intelligence: Re-Imagining Big Data’s Applicability appeared first on Artificial Intelligence.