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) …
Scientists have developed a fibre that combines the elasticity of rubber with the strength of a metal. Researchers at North Carolina State University are behind the innovation, which has created a tougher material that could be incorporated into soft robotics, packaging materials or next-generation textiles. The team made fibres consisting of a gallium metal core surrounded by an elastic polymer sheath. When placed under stress, the fibre has the strength of the metal core. But whereas the metal eventually breaks, the fiber doesn't fail - the polymer sheath absorbs the strain between the breaks in the metal and transfers the stress back to the metal core.
As Chief Data Scientist, Adam leads the data science, data engineering and research teams at RiskIQ. Adam also has received patents for identifying new external threats using machine learning. Adam received his Ph.D. in experimental particle physics from Princeton University. As an award-winning member of the CMS collaboration at the Large Hadron Collider, he was an integral part in developing the online and offline analysis systems that lead to the discovery of the Higgs Boson.
Productionizing machine learning/AI/data science is a challenge. Not only are the outputs of machine-learning algorithms often compiled artifacts that need to be incorporated into existing production services, the languages and techniques used to develop these models are usually very different than those used in building the actual service. In this post, I want to explore how the degrees of freedom in versioning machine learning systems poses a unique challenge. I'll identify four key axes on which machine learning systems have a notion of version, along with some brief recommendations for how to simplify this a bit. Consider the following: you run a large webservice on a JVM-based stack, and now you want to incorporate a machine learning model. You have data scientists, and they have spent some time doing the research, and now they are ready to deliver their work product: a proof-of-concept model built in R, and you have to implement this somehow. When none of your data scientists are backend engineers and none of your backend engineers speak R. Most of these questions don't have an accepted answer in the way that we have accepted answers in the world of app development, for instance.
Microsoft on Thursday announced a number of artificial intelligence, mixed reality and customer insights tools for Microsoft Dynamics 365 at its Microsoft Business Forward event in Paris. There is "a lot of hype around AI and MR," noted Rebecca Wettemann, vice president of research at Nucleus Research, "but these are real applications delivering value to customers today." Microsoft also announced it will publicly preview a new Microsoft Forms Pro this spring. There is "a consistent theme in all these offerings -- providing high degree of flexibility to enable citizen developers and power users while still maintaining technology governance and compliance," remarked Nicole France, principal analyst at Constellation Research. "Microsoft's advantage is its decades of investment in R&D across its portfolio, which it's bringing to bear on its business applications portfolio," Wettemann told CRM Buyer.
In the early 2000s, the U.K. government started pushing a new angle to narrow the inequality gap: getting people access to financial services, such as a bank account, money advice and affordable credit. It saw a strong link between financial exclusion and child poverty, and a 2004 Treasury report found more than 65 percent of the unbanked were on the lowest salaries. That's when Frédéric Nze had the idea that eventually led to Oakam, a small loans company with a mission to underwrite customers who typically struggle to get a loan. In 2017, a parliamentary committee called on Britain's financial regulator and banks to give it a greater priority. More than 1.7 million people in the U.K. do not have a bank account, and 40 percent of the working-age population have less than 100 pounds in savings.
In a deal that made few ripples outside the energy industry, two very large but relatively obscure companies, Rockwell Automation and Schlumberger Limited, announced a joint venture called Sensia. The new company will "sell equipment and services to advance digital technology and automation in the oilfield," according to the Houston Chronicle. Yet the partnership has ramifications far beyond Houston's energy corridor: It's part of a growing trend that sees major tech companies teaming with oil giants to use automation, AI, and big data services to enhance oil exploration, extraction, and production. Rockwell is the world's largest company that is dedicated to industrial automation, and Schlumberger, a competitor of Halliburton, is the world's largest oilfield services firm. Sensia will be, according to the press release, "the first fully integrated digital oilfield automation solutions provider."
Quickly build simple chatbots and advanced digital assistants that let your customers engage in natural conversations with your business. Deployable on websites, mobile apps, messaging apps, and through voice interfaces, Oracle Digital Assistant extends and enhances the functionality of your back-end systems, delivering personalized and engaging experiences to each user.
Two year ago we wrote about the 3 real uses of Artificial Intelligence and Machine Learning, within the two years a lot has changed in the tech world as development continues. As always AI and machine learning is a hot topic, with conversations not just around how it is going to affect our lives, but also the risks it poses to jobs. There is also a hefty debate around the ethics of AI at the moment. With that being said we want to take a look at how much the industry has really changed in this time, and how our 3 real uses of AI and machine learning have developed. While these apps are using machine learning to be able to answer and aid your question they are also learning about you.
In these cash-poor times, businesses are struggling to stay ahead of the competition -- and many of them are missing opportunities to talk to their customers. UK-based digital marketing agency Marketing Signals carried out a survey to find out what business leaders planned for the year ahead. It surveyed 1,021 UK workers to find out what opportunities they were missing. The survey showed that over one in three (36 percent) business leaders are unsure of their digital marketing strategy for 2019. Gareth Hoyle, managing director at MarketingSignals.com said: "The research shows how there are a number of missed opportunities for companies when it comes to their digital marketing activity, particularly with regard to organic performance. This is to some extent understandable as algorithms are regularly updating and it can be hard for business owners to keep up with the very latest developments."