If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
AI and ML present a new dawn in the cybersecurity industry. AI is not a new concept to computing. It was defined in 1956 as the ability of computers to perform tasks that were characteristic of human intelligence. Such tasks included learning, making decisions, solving problems, and understanding and recognizing speech. ML is a broad term referring to the ability of computers to acquire new knowledge without human intervention. ML is a subset of AI and can take many forms, such as deep learning, reinforcement learning, and Bayesian networks.
Now we have our API set up; we can begin pulling tweet data. We will focus on Tesla for this article. We will be using the requests library to interact with the Twitter API. We can search for the most recent tweets given a query through the /tweets/search/recent endpoint. We need two more parts before sending our request, (1) authorization and (2) a search query.
Telling humans apart and following them as they move in their surrounding environment could be two highly valuable skills for service robots. In fact, when combined, these two capabilities would allow robots to follow specific people as they are interacting with them or offering their assistance. Researchers at Monash University, JDQ Systems and University of British Columbia recently developed a service robot designed to assist residents at elderly care homes or patients at other healthcare facilities. In a paper pre-published on arXiv, they presented a computational technique that allows their robot to identify and track people in its vicinity, following specific users as they are assisting them. "Our team has been developing a socially assistive robot platform, Aether, for providing daily routine assistance to staff and residents at elderly care and assisted living facilities," Wesley P. Chan, one of the researchers who carried out the study, told TechXplore.
The UK government recently published a review of algorithmic bias – an important and even crucial subject as ever more decision-making progresses from wetware to silicon. However, it would have been useful if they'd understood what Gary Becker told us all about discrimination itself – work for which he won the Nobel prize for economics. Almost all the things they are worrying about solve themselves within his logical structure. First though, a linguistic structure – let's examine the difference between algorithms and artificial intelligence (AI). An algo doesn't have to be encoded at all, it's a set of rules by which to make a decision – usually, almost always, derived from the current methods by which we make such decisions, just formalised or even coded.
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.