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) …
Oracle's cloud business is still a blip on the radar compared to the competition, but it's the only cloud provider offering security and automation features uniquely built for the enterprise, Oracle co-founder and CTO Larry Ellison argued Monday. "Other clouds have been around for a long time, they were not really designed for the enterprise," Ellison said in his keynote address at the OpenWorld conference in San Francisco. Oracle is now selling its Generation 2 Cloud, which is available in the public cloud and will be available next year with Cloud@Customer, one of Oracle's most popular products. The most important part of the Gen 2 Cloud, Ellison said, is the autonomous database. Taking aim at cloud giant Amazon Web Services, Ellison compared the autonomous database to the reported development of an AWS semi-autonomous database.
Spektacom, a sports tech startup founded by Anil Kumble, one of the most accomplished cricketers in India, partnered with Microsoft to bring cutting-edge technology to the game of cricket. Spektacom built a platform that includes a 5-gram sticker that attaches itself to the cricket bat, a stump box that acts as an IoT gateway, and AI-powered analytics to deliver insights on the batting style of a batsman. The data collected in the cloud is instantly run through a machine learning model that assesses the quality of a shot. Anil officially calls the IoT-enabled bat as a power bat, which doesn't deviate from the specifications of a standard cricket bat. The technology behind the power bat is fascinating.
At the Oracle OpenWorld conference this week, Oracle is rolling out a series of updates to its cloud applications portfolio, infusing more automation and intelligence into ERP, HCM, CX and Data Cloud applications. It's also expanding its SaaS portfolio to include subscription management services. By embedding AI into its SaaS products, Oracle intends to show at this year's OpenWorld "how we help customers change their business processes and business models, and help shape the future at large around machine learning, AI and adapted intelligence," Juergen Lindner, SVP of SaaS product marketing at Oracle, said to ZDNet. The Internet of Things is the new frontier. However, generations of ERP systems were not designed to handle global networks of sensors and devices.
In the very near future, AI will transform the way we live – whether that's how our cities operate and flow, how services are delivered, or even the ways in which we interact with buildings and facilities – or them with us. Every activity will be streamlined, connected and optimised through the responsible collection of data and the identification of correlations and potential improvements. This same logic will then manifest in the way we work. Our productivity, speed, accuracy, insightfulness and innovation will all be enhanced through exploiting patterns in data that would be invisible to the more limited human brain. But before this ideal can be realised, there are plenty of steps yet to be taken.
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. One of ML's most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation. Unlike the stories that abound about large enterprises, the guy had neither expertise in machine learning, nor a big budget. But he did manage to get familiar with TensorFlow and employed deep learning to recognize different classes of cucumbers. By using machine learning cloud services, you can start building your first working models, yielding valuable insights from predictions with a relatively small team. We've already discussed machine learning strategy. Now let's have a look at the best machine learning platforms on the market and consider some of the infrastructural decisions to be made.
A mid-sized company with about 5,000 employees gets approximately 1,000 to 2,000 security incidents per day. This equates to nearly 60,000 threat incidents per month and as many as 720,000 per year. That number has increased dramatically because of automated security attacks using bots. According to The Cybersecurity Intelligence Report from Oracle Dyn "over 50% of internet traffic is bots. With these huge numbers, there are just too many threat incidents for the typical security operations team to manage with any level of precision.
Finance departments have been racing around for decades to try to keep up with the increasing quantity and complexity of information. But the data and workloads keep growing. APQC's recent annual survey, "Where Does the Time Go in Finance?" shows that in spite of significant success reducing costs, transaction processing takes up almost half of a finance department's time. This could be holding finance teams and their leaders back from taking on a more strategic role in emerging digital business models. As APQC put it: "This means that in an average work week, highly paid finance staff are spending the equivalent of Monday morning through lunchtime on Wednesday making sure that bills get paid, customers get accurate invoices, general accounting work gets done and fixed assets are accounted for, among many other tasks that keep the money moving through an organization."
This blog post is co-authored by Jaya Mathew and Francesca Lazzeri, data scientists at Microsoft. The Artificial Intelligence Conference in London is a relatively addition to the list of conferences hosted by O'Reilly worldwide. The aim of this conference is to create a forum for the ever-growing AI community to explore the most essential issues and innovations in applied AI. In the conference the various talks covered topics ranging from practical business applications of AI, to compelling AI enabled use cases, to various technical trainings and deep dive into successful AI projects etc. In our session "A day in the life of a data scientist in an AI company", we presented a scientific framework to help organizations to systematically discover opportunities to create value from data, qualify new opportunities and assess their fit and potential, then how to build a team to smoothly implement end-to-end advanced analytics pilots and projects, and produce sustainable ongoing business value from data.
The shift of focus from'under the hood' to the'car console' has opened doors for advanced technologies such as artificial intelligence, cloud computing, big data and machine learning into the automobile world. These technologies are instrumental in bridging the gap between fiction and reality when we talk about connected and autonomous vehicles. Here's a how Microsoft (MSFT) has made inroads into the connected car space. Cognizant defines the connected car as, "a vehicle using mechatronics, telematics and artificial intelligence technologies to interact with the environment to provide greater safety, comfort, entertainment and, importantly, a'connected-life' experience." The connected car is deemed to save considerable time and resources, while making mobility more efficient, safer and enjoyable.