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 (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.
Artificial Intelligence is a hot topic. Applications based on machine learning make the news on a near-daily basis. Smart police cameras steered by algorithms can register drivers holding cell phones with great precision. Algorithms can dynamically determine the real-time prices for taxi rides, hotel rooms, airplane seats, and so on. High-frequency traders are getting rich as they sleep by letting their secret algorithms do the work.
You have most probably heard of AI or artificial intelligence. You hear it being used as a theme in movies like "The Terminator" and other stories set in a technologically-advanced world, you read it on books and you even see it on the branding of several products such as smartphones. There is what is called "AI photography," "AI gaming," and a lot of other things branded as "AI." Even in gaming, AI is plastered all over, In the world of online gambling, the top online betting apps such as Betway India make use of "AI" to run their betting apps without the need to be manually controlled by a human. However, are the things that people call AI nowadays really what AI is? Are these things really what makes AI intelligent? What is AI, and how does it work?
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UN Global Pulse is a United Nations initiative doing pioneering work at the nexus between development aid and technology. It is conducting research into the potential of Big Data and artificial intelligence in addition to supporting other UN agencies in the implementation of projects. In response to the pandemic, governments around the world have grown increasingly interested in data-focused models that might be able to forecast the spread of COVID-19 infections and the effectiveness of planned strategies along with their possible side effects. The innovation team at the UN Refugee Agency (UNHCR) has already developed a number of experimental approaches to gather clues about possible future migration events. Data analysts, for example, have used open source weather data and Facebook postings from migrant traffickers for clues about smuggling prices, the most frequently used routes and assembly points.
Big data has been called a strategic asset, a competitive advantage, and for a short while, was often referred to as "the new oil." Today, the idea that data is valuable isn't exactly news. However, many organizations don't have a reliable way to transform raw data into action. Today's businesses have access to massive amounts of data, yet it's becoming harder to manage all of those data points, identify what's important, and chart a path toward making meaningful improvements. In this article, we'll provide an augmented analytics definition, explore it's business intelligence (BI) origins, and discuss the game-changing potential just on the horizon.
The development of the internet over the last few decades has resulted in a massive increase in the production of data and the unprecedented availability of computing power for corporate applications. Machine Learning and artificial intelligence (AI) techniques have been fuelled by these revolutions to emerge from being purely academic topics of investigation to be the basis for a new wave of products and services for the digital age. The paradigm-shifting opportunities presented to corporates by this emerging technology range from the ability to expose and extract insights and patterns from data lakes to replacing human beings in critical decision-making scenarios. However, with these opportunities also come novel risks and concerns that must be considered when contemplating the development and deployment of AI and machine learning agents. These include understanding how their trustworthiness may be measured, the ethics and policies required for their deployment and the cybersecurity implications of their widespread adoption.