The graph represents a network of 1,276 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 25 September 2020 at 10:21 UTC. The requested start date was Friday, 25 September 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 17-hour, 36-minute period from Wednesday, 23 September 2020 at 06:09 UTC to Thursday, 24 September 2020 at 23:45 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Spam filtering, face recognition, recommendation engines -- when you have a large data set on which you'd like to perform predictive analysis or pattern recognition, machine learning is the way to go. The proliferation of free open source software has made machine learning easier to implement both on single machines and at scale, and in most popular programming languages. These open source tools include libraries for the likes of Python, R, C, Java, Scala, Clojure, JavaScript, and Go. Apache Mahout provides a way to build environments for hosting machine learning applications that can be scaled quickly and efficiently to meet demand. Mahout works mainly with another well-known Apache project, Spark, and was originally devised to work with Hadoop for the sake of running distributed applications, but has been extended to work with other distributed back ends like Flink and H2O. Mahout uses a domain specific language in Scala.

The 8 Ivy League schools are among the most prestigious colleges in the world. They include Brown, Harvard, Cornell, Princeton, Dartmouth, Yale, and Columbia universities, and the University of Pennsylvania. All eight schools place in the top fifteen of the U.S. News and World Report national university rankings. These Ivy League schools are also highly selective and extremely hard to get into. But the good news is that all these universities now offer free online courses across multiple online course platforms.

AI can impact and transform business IT operations. The technology has never been more useful as IT teams look to enable mass remote working during the Covid-19 pandemic. Nick McQuire, SVP, Enterprise Research at CCS Insight, explains that "the area of IT Helpdesk support has become a big use case for AI especially in the context of remote IT operations during the pandemic and we have seen the domain become a big focus for the likes of IBM recently with the launch of Watson AIOps, for example." Generally, he points out that AI can help business IT operations quickly diagnose problems and handle support tickets through greater automation. In some cases, "IT is also able to proactively fix problems based on predicting when an issue will arise," McQuire adds. Dr Iain Brown, head of data science at SAS UK & Ireland, says that it is "no understatement to say that AI can revolutionise business IT operations.

C is ideal for dynamic load balancing, adaptive caching, and developing large big data frameworks, and libraries. Google's MapReduce, MongoDB, most of the deep learning libraries listed below have been implemented using C . Scylla known for its ultra-low latency and extremely high throughput is coded using C acts as a replacement to Apache Cassandra and Amazon DynamoDB. With some of the unique advantages of C as a programming language, (including memory management, performance characteristics, and systems programming), it definitely serves as one of the most efficient tools for developing fast scalable Data Science and Big Data libraries. Further, Julia (a compiled and interactive language – developed from MIT) is emerging as a potential competitor to Python in the field of scientific computing and data processing. Its fast processing speed, parallelism, static along with dynamic typing and C bindings for plugging in libraries, has eased the job for developers/data scientists to integrate and use C as a data science and big data library.

After decades in research labs, machine learning is now getting enormous attention for real-world applications that harness the technology's formidable power to discern patterns in huge quantities and types of data at high speed: fraud detection, customer 360, facial recognition, workflow management, shopping personalization and much more. The payback of such initiatives can be big. But even greater opportunities lie in creating advanced analytic systems that use machine learning's unmatched ability to see, organize and leverage insights from ever-growing mounds of data to unlock the deep, transformative potential of Big Data and the Internet of Things. To get to the next level of machine learning, companies must develop a sound business case; implement machine learning algorithms for speed at scale; use systems equipped with processors with multiple integrated cores, faster memory subsystems, and develop architectures that can handle massive amounts data in real time. For many organizations, it is an ideal time to build on or begin machine-learning experience, deepen knowledge, and reap the benefits and competitive advantages this sophisticated data analytics technology can provide.

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. The perception of legacy enterprise business intelligence (BI) platforms comes with some legitimate stigma and baggage. It's technology first, not business-led; the graphical user interface (GUI)-based user experience (UX) doesn't address ease of use for all business decision-makers; there are too many underutilized reports and dashboards floating around in the enterprise; and signals produced by BI applications aren't actionable, resulting in a disconnect between BI and tangible business outcomes. So, is enterprise BI dead? If I got $1,000 every time I heard the phrase "BI is dead" over my 30-plus-year career, I'd be a very rich man.

Machine learning (ML) is quickly becoming a mainstay of the enterprise business world, yet entrepreneurs and small-business owners may shy away from investing in it. While you may not fully understand the ins and outs of ML or how it can benefit your small business, you can still make effective use of the technology without being an expert in it. We asked a panel of Forbes Technology Council members to share some smart ways entrepreneurs and small-business owners can leverage ML. Most ML models will require tons of data (the majority of them require supervised learning), which translates into a large effort that most entrepreneurs and small-business owners can't sustain. One approach is to leverage SaaS/PaaS services, such as the AWS portfolio of pre-trained artificial intelligence (AI) services: Comprehend, Rekognition, Lex, Personalize, Translate, Polly and others, each tailored to a specific domain.

With a large percentage of the global workforce based remotely for the foreseeable future, more business than ever is being conducted over email. And while this modern convenience has been critical to the continued operation of many businesses in the current health crisis, it has also presented those businesses with new data security challenges. The unfamiliar environment of remote work -- not to mention its potential distractions, like children and pets -- leaves employees more vulnerable to misdirected emails and other mistakes that can lead to accidental data breaches. Scams aimed at both individuals and organizations (even healthcare facilities on the front lines of the pandemic have not been immune to their efforts) have also risen, attempting to capitalize on the situation. Accidental breaches are notoriously difficult to combat because they can be caused by something as simple as a typo in an email address.

Financial crime as a wider category of cybercrime continues to be one of the most potent of online threats, covering nefarious actives as diverse as fraud, money laundering and funding terrorism. Today, one of the startups that has been building data intelligence solutions to help combat that is announcing a fundraise to continue fueling its growth. Ripjar, a UK company founded by five data scientists who previously worked together in British intelligence at the Government Communications Headquarters (GCHQ, the UK's equivalent of the NSA), has raised $36.8 million (£28 million) in a Series B, money that it plans to use to continue expanding the scope of its AI platform -- which it calls Labyrinth -- and scaling the business. Labyrinth, as Ripjar describes it, works with both structured and unstructured data, using natural language processing and an API-based platform that lets organizations incorporate any data source they would like to analyse and monitor for activity. It automatically and in real time checks these against other data sources like sanctions lists, politically exposed persons (PEPs) lists and transaction alerts.