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Information Technology

Intel details chips designed for IoT and edge workloads


Intel today announced the launch of new products tailored to edge computing scenarios like digital signage, interactive kiosks, medical devices, and health care service robots. The 11th Gen Intel Core Processors, Atom x6000E Series, Pentium, Celeron N, and J Series bring new AI security, functional safety, and real-time capabilities to edge customers, the chipmaker says, laying the groundwork for innovative future applications. Intel expects the edge market to be a $65 billion silicon opportunity by 2024. The company's own revenue in the space grew more than 20% to $9.5 billion in 2018. And according to a 2020 IDC report, up to 70% of all enterprises will process data at the edge within three years.

Global Big Data Conference


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.

AI Concepts for Security (GSX 2020)


Nearly all security cameras available today have some form of video analytics on board, according to Brian Baker, vice president, Americas, for Calipsa, a leading provider of deep learning-powered video analytics for false alarm reduction. But why is this the case? And what do facilities managers need to know about it? Video analytics powered by artificial intelligence promise smarter alerts that free your security staff from responding to false alarms, says Baker, a presenter at the 2020 GSX virtual tradeshow. But to find the right AI-backed analytics for your organization, it's first important to understand the basic concepts behind the technologies.

Score streaming data with a machine learning model


This is part of the Learning path: Get started with IBM Streams. In this developer code pattern, we will be streaming online shopping data and using the data to track the products that each customer has added to the cart. We will build a k-means clustering model with scikit-learn to group customers according to the contents of their shopping carts. The cluster assignment can be used to predict additional products to recommend. Our application will be built using IBM Streams on IBM Cloud Pak for Data.

Council Post: 14 Smart Ways To Leverage Machine Learning For Small Businesses


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.

How creative artificial intelligence (AI) and fashion meet


Artificial intelligence (AI) in fashion is no longer a secret and has widely been used to mostly help businesses to streamline processes and increase sales. But the skillsets of fashion designers and computer scientists are miles apart, so it's not until recently that the creative applications of AI in this industry have been explored. "Initial uses of artificial intelligence have focused on quantifiable business needs, which has allowed for start-ups to offer a service to brands," Matthew Drinkwater, head of the fashion innovation agency (FIA) at London College of Fashion (LCF), told Forbes. "Creativity is much more difficult to quantify and therefore more likely to follow behind." Seeing the opportunity for AI to play a bigger role in the creative process, LFC has launched an AI course aiming to develop creative fashion solutions and experiences that challenge the current approaches to fashion design.

Computational Needs for Computer Vision (CV) in AI & ML Systems


Computer vision (CV) is a major task for modern Artificial Intelligence (AI) and Machine Learning (ML) systems. It's accelerating nearly every domain in the tech industry enabling organizations to revolutionize the way machines and business systems work. Academically, it is a well-established area of computer science and many decades worth of research work have gone into this field. However, the use of deep neural networks has recently revolutionized the CV field and given it new oxygen. There is a diverse array of application areas for computer vision.

Scientists Imagine Worldwide Real-Time Dream Reporting Assisted by AI - AI Trends


Scientists are developing AI tools to help analyze dreams, in the hopes of better understanding where dreams come from and helping people address real-life problems, especially around mental health. Scientists in the UK and Italy have created an AI tool to analyze dream reports, which are text reports written by the dreamer when they wake up. The analysis of dream reports previously demanded a time-consuming manual annotation of text, which is why dream reports have recently been mined with algorithms focused on identifying emotions, according to a recent account in the Royal Society Open Science journal. The goal is to mine important aspects of dream reports, such as characters and interactions, in a principled way grounded in academic literature. The team designed a tool that automatically scores dream reports based on a widely-used dream analysis scale.

Council Post: Is Your Business Ready To Implement AI?


Businesses are increasingly turning to AI to improve their critical processes and become more agile, especially in times like these when the market is constantly and rapidly changing. AI can be deployed in dozens of ways that save on time and costs, from automating back-office operations, to rapidly expanding product catalogs, to prioritizing human actions in the workplace, to increasing the speed at which a company can profitably adapt their sales strategies. It's almost always worth it to start using AI, regardless of company size or industry, because once your enterprise is ready for AI, it's possible to ramp up the number of automated tasks over time and save more and more in time and costs. However, AI implementation is not instantaneous. It takes preparation to ensure that the solutions you've chosen for your business are the right ones and that they will be capable of benefiting your business the way you envision.

Go Ahead, Try to Sneak Bad Words Past AI Filters--for Research


Facebook's artificial intelligence researchers have a plan to make algorithms smarter by exposing them to human cunning. They want your help to supply the trickery. Thursday, Facebook's AI lab launched a project called Dynabench that creates a kind of gladiatorial arena in which humans try to trip up AI systems. Challenges include crafting sentences that cause a sentiment-scoring system to misfire, reading a comment as negative when it is actually positive, for example. Another involves tricking a hate speech filter--a potential draw for teens and trolls.