NEW YORK, NY -- December 4, 2020 -- IBTM announced today that CLIPr, a technology company that applies AI and machine learning to create searchable recaps of meetings and events, is a finalist of the organization's prestigious TechWatch Awards. Finalists will be presenting their technologies before a panel of judges during the 2020 IBTM World Virtual event, which is planned to run virtually December 8–10, 2020. IBTM World is the leading global event for the meetings, incentives, conferences and events (MICE) industry. The organization brings together people from all over the world who share the common vision that business results improve when the right connections are made. The annual event is organized by Reed Exhibitions, and like most events this year, was moved online due to the coronavirus.

For analytics professionals, there has been a pursuit for getting more and more included in the consumption of analytics and not only creation. The value addition and impact derivation from all the analytics and data science efforts depends on the acceptance and appreciation of the process by the stakeholders, not only results. The impact of analytics is a long term cultural evolution than making short term decisions only. The reason creators and consumers have been debating the value of analytics and roadblocks in the maturity roadmap is because of the fact that the consumers of analytics expect data science to be full course meal or a gourmet cuisine. Analytic is not for one time satiation, it is to be savoured right from the time it starts cooking Make them learn the recipe, before they enjoy the meal Customer education is the most important part of analytics adoption in an organization.

Artificial intelligence-powered use cases for climate action could help organisations meet up to 45% of the Economic Emission Intensity (EEI) targets of the Paris Agreement. New research from the Capgemini Research Institute has found that while AI offers many climate action use cases, only 13% of organisations are successfully combining climate vision with AI capabilities. AI use cases include improving energy efficiency, reducing dependence on fossil fuels and optimising processes to aid productivity. The research found that 67% of organisations have long-term business goals to tackle climate change. While many technologies address a specific outcome, such as carbon capture or renewable sources of energy, AI can accelerate organisations' climate action across sectors and value chains.

No one seems to have investment dollars, patience, or the right skill sets in their manufacturing departments, along with a sage-like understanding of the applications and data to really drive adoption and value in manufacturing. And we see existing companies already starting out with near insurmountable challenges just in core fundamental items, let alone these advanced concepts. For example, most companies don't have a single type of Bill of Material (BOM) construct. They don't share a commonly governed set of master data – item master, vendor, customer, chart of accounts, etc. They have multiple code sets and versions of ERP and MES software, and different PLCs and sensors capturing data, so that if they ever did get patience and investment capability, they would be unable to build and maintain all of the cross references and algorithms required because of all of the different systems and master data.

Chatbots have become an increasingly important channel for businesses to service their customers. Chatbots provide 24/7 availability and can help customers interact with brands anywhere, anytime and on any device. To effectively utilize chatbots, they must be built with good design, development, test, and deployment practices. This post provides you with a framework that helps you automate the testing processes and reduce the overall bot development cycle for Amazon Lex bots. Amazon Lex is a service for building conversational interfaces into any application using voice and text.

"What's really important here is the method and how that method applies to other applications," says Joy Buolamwini, a researcher in the MIT Media Lab's Civic Media group and first author on the new paper. "The same data-centric techniques that can be used to try to determine somebody's gender are also used to identify a person when you're looking for a criminal suspect or to unlock your phone. I'm really hopeful that this will spur more work into looking at [other] disparities." Buolamwini is joined on the paper by Timnit Gebru, who was a graduate student at Stanford when the work was done and is now a postdoc at Microsoft Research. The three programs that Buolamwini and Gebru investigated were general-purpose facial-analysis systems, which could be used to match faces in different photos as well as to assess characteristics such as gender, age, and mood. All three systems treated gender classification as a binary decision -- male or female -- which made their performance on that task particularly easy to assess statistically.

Machine learning is a science that uses existing data on a subject to train a computer how to identify related data. Just like with humans, the more training a machine learning algorithm gets, the more likely it is to succeed at its task. We have an extensive amount of information on attacks that can be used to train machines. After all, new attacks come out every day and over a hundred million malware samples have been collected each year since 2014. This information, as well as the historical information, can be fed into machine learning algorithms to better understand the attacks that haven't happened yet.

In the past year, lockdowns and other COVID-19 safety measures have made online shopping more popular than ever, but the skyrocketing demand is leaving many retailers struggling to fulfill orders while ensuring the safety of their warehouse employees. Researchers at the University of California, Berkeley, have created new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments. The technology is described in a paper published online today (Wednesday, Nov. 18) in the journal Science Robotics. Automating warehouse tasks can be challenging because many actions that come naturally to humans -- like deciding where and how to pick up different types of objects and then coordinating the shoulder, arm and wrist movements needed to move each object from one location to another -- are actually quite difficult for robots. Robotic motion also tends to be jerky, which can increase the risk of damaging both the products and the robots.

Source: Rob Felt, Georgia Institute of Technology Headlines regularly proclaim that robots are coming for people's jobs or are "creepy," but both robotics developers and the general public are increasingly aware of the many ways in which the technology can boost productivity and safety. However, the need to understand how robots and artificial intelligence can inherit negative human biases is still urgent, according to roboticist Ayanna Howard. "Bias in AI is the responsibility of the designer," said Howard, who recently published the book Sex, Race, and Robots: How to Be Human in the Age of AI. "Most designers and developers are fairly homogenous -- largely male. I'm a roboticist, but my advisor was male, so the thinking processes were driven by male perspectives and are a product of training." "We need different people in terms of life experience," she told The Robot Report.

BEGIN ARTICLE PREVIEW: Bay Area-based Rapid Robotics today announced it has raised $5.5 million in seed funding in a round led by Greycroft and Bee Partners. The announcement comes during what has been a solid several months for robotics funding, and more and more companies are looking to automate workforces as the COVID-19 pandemic has ground a lot of productivity to a halt. Manufacturing is one of the sectors of greatest interest on that front, as a business that can’t really afford to go on hiatus. That positions Rapid Robotics fairly well in the field. There are, of course, countless companies vying for a space in the massive industry. Rapid’s primary value prop is in the training category. Getting robotics up and running in a factory can by an expensive and time-i