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) …
Goonhilly Earth Station opened its new data center and launched a managed High Performance Computing (HPC) platform for Artificial Intelligence (AI) and Machine Learning (ML) computing on demand. Goonhilly's goal is to create a U.K. hub for AI and ML services that acts as a marketplace and allows academia and enterprise to collaborate and share ideas. One of the first organizations in the U.K. to deploy a liquid immersion cooling system from Submer Technologies to mitigate the power demands of HPC, Goonhilly's platform is designed to meet the data-intensive needs of the automotive, life sciences and space/aerospace marketplaces. Additionally, its onsite array of solar panels can support the data center's full power requirements of 500 Kilowatts (KW). The new managed platform delivers high performance GPU-based compute and storage for decentralised and centralised AI and machine learning applications to meet the data-intensive needs of the automotive, life sciences and space/aerospace marketplaces.
As we all know that production has seen tremendous benefits from automation, Lean, and advanced IT, Artificial Intelligence guarantees to become the next productivity development breakthrough. Artificial Intelligence (AI) does have the potential to improve and expand human capacity as well as help businesses faster, achieve more, and more effectively. Although not a new concept at all, several more recent developments have made it possible for AI to cross into the mainstream: cloud computing, big data, and improved algorithms for machine learning. AI-driven analytics, and real-time insights, have begun to help the company grow its revenues as well as market shares quicker than the peers in sectors as diverse as health care, utility finance, and ecommerce. The maker had spoken to Jamie Hall, senior alternatives expert for Microsoft, to learn as much about the opportunities that Artificial Intelligence retains for manufacturing organizations.
This is, what we aim to at our own factories. Read my introduction to a series of blogposts how we do this. Harnessing the power of artificial intelligence (AI), engineers at our manufacturing plant in Amberg can predict when a key component is likely to fail – up to 36 hours before the failure actually happens. This allows them to react in plenty of time to avoid a costly breakdown of the machine. In our electronics manufacturing facility in Amberg, we have several PCB cutting machines that are deployed for a number of our SIMATIC products – including the S7-300 and ET 200.
After having worked with several VCs and startups, I noticed that successful startups tend to share the same characteristics. Hence, I decided to write an article that could help entrepreneurs understand what can make an AI startup catch investors' attention. As always, and most particularly, the findings detailed below stem from my experience. The main challenge for AI startups is to prove to investors the scalability of their business model. Over the years, I noticed that some consumer products powered by automation were randomly/easily using the term AI in their communication when they actually only relied on data analytics to automate low added-value tasks.
Below we will explain what this new ML technology can do for you as well as lay out some best practices for getting the best results! The automatic feedback categorisation technology is the first of several new, built-in technologies to be launched by Mopinion, which will demonstrate the latest advancements in artificial intelligence and machine learning. It employs Supervised Machine Learning (ML) techniques that make the analysis of qualitative feedback data – by way of labelling and categorisation – a much more fluid and systematic process. Essentially with Supervised Machine Learning you are training the system to do what you want by providing it with examples to learn from. In other words, the technology takes historical data – which in this case is any labelling or categorisation the user has previously inputted into the software – and predicts how the feedback should be categorised based on that data.
Intelligent, learning, autonomous machines are about to change the way we do business forever. But in a world where corporations or even executives may be liable in a civil or even criminal court for their decisions, who is responsible for decisions made by artificial intelligence (AI)? In the United States, courts are already having to wrestle with this science fiction scenario after an Arizona woman was killed by an experimental autonomous Uber vehicle. The European Commission recently shared ethical guidelines, requiring AI to be transparent, have human oversight and be subject to privacy and data protection rules. This sounds really good, but how will any of this be applied in practical situations?
That, by default, means that AI has the potential to change people's jobs, especially for those involved in rote transactional tasks (e.g., automatically emailing a receipt following an online purchase). However, AI is increasingly taking on cognitive tasks that previously required a human touch, such as conversational AI providing customers with 24/7 access to information and services. While automation enables companies to spend less money to achieve the same (or better) results, decision makers would be wise to keep in mind the other opportunities AI opens – particularly the ability to reapply human capital elsewhere within their businesses. AI should not be viewed as a replacement for human workers, but rather as a means to free humans from the repetitive (and in some cases rather boring) tasks that define so much of the modern work day. It would be understandable that this transition is a source of trepidation for some workers, but it's nothing the marketplace hasn't experienced before.
Tesla has reigned over the electric car market for over a decade, but these new autos are hoping to give Tesla a run for their money. Current and former Tesla employees working in the company's open-air "tent" factory say they were pressured to take shortcuts to hit aggressive Model 3 production goals, including making fast fixes to plastic housings with electrical tape, working through harsh conditions and skipping previously required vehicle tests. For instance, four people who worked on the assembly line say they were told by supervisors to use electrical tape to patch cracks on plastic brackets and housings, and provided photographs showing where tape was applied. They and four additional people familiar with conditions there describe working through high heat, cold temperatures at night and smoky air during last year's wildfires in Northern California. Tesla can't appeal to women: Electric cars, Elon Musk may be off-putting Why I bought a Tesla: One woman's experience buying Elon Musk's sleek EV Their disclosures highlight the difficult balance Tesla must strike as it ramps up production while trying to stem costs. Tesla recently told shareholders that in the three months ending June 30, 2019, it made 87,048 vehicles, including 72,531 Model 3s, the company's lowest-priced sedan.
RESEARCH TRIANGLE PARK, N.C., July 16, 2019 (GLOBE NEWSWIRE) -- JAGGAER, the world's largest independent spend management company, announces an upcoming webinar with a deep analysis of the actionable application of Artificial Intelligence (AI) in the procurement process. The webinar is scheduled for Tuesday, July 23rd, 11-11:45 AM ET: Register here. This webinar is designed to address AI in companies that have already integrated a program into their digital transformation roadmap, and companies that are in the midst of considering an investment. Through applying Big Data with AI, the procurement function becomes an important provider of insights and guidance for increasingly complex supply chains. The resulting information yields better and more precise decisions with lower costs, however arriving at this state requires procurement departments to initiate new ways of thinking and acting, to support the adoption of new technologies.
One of the holy grails in the world of advertising and marketing has been finding a way to accurately capture and understand what consumers are doing throughout the day, regardless of whether it's a digital or offline activity. That goal has become even more elusive in recent years, with the surge of regulations around privacy and data protection that limit what kind of information can be collected and used. Now, a startup believes it's cracked the code, and it's raised a large round of funding that underscores its success so far and what it believes is untapped future demand. Near, which has built an interactive, cloud-based AI platform called AllSpark that works across 44 countries to create anonymised, location-based profiles of users -- 1.6 billion each month at present -- based on a trove of information that it sources and then merges from phones, data partners, carriers and its customers, but which it claims was built "with privacy by design", has raised $100 million. The company believes that this Series C -- from a single backer, Great Pacific Capital out of London -- is one of the biggest rounds ever to be raised in this particular area of marketing technology.