Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users. Getting started with machine learning is hard. Even to run the most basic of experiments takes a good amount of expertise. All of these new tools greatly simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.
Imagine you're hiking through the woods near a border. Suddenly, you hear a mechanical buzzing, like a gigantic bee. Two quadcopters have spotted you and swoop in for a closer look. They send the signals to a central server, which triangulates your exact location and feeds it back to the drones. Cameras and other sensors on the machines recognize you as human and try to ascertain your intentions.
Hospitals and medical practices are already using a fair amount of automation. Some hospitals are set up for delivery robots to open remote-control doors and even use elevators to get around the building. Robots can also assist with more complex tasks, like surgery. Their participation can range from simply helping stabilize a surgeon's tools all the way to autonomously performing the entire procedure. Perhaps the most famous robotic surgery system lets a surgeon operate full-size, ergonomically friendly equipment as a remote control to direct extremely tiny instruments what to do inside a patient's body, often through extremely small incisions.
Over the past decade, designers have developed silicon technologies that run advanced deep learning mathematics fast enough to explore and implement artificial intelligence (AI) applications such as object identification, voice and facial recognition, and more. Machine vision applications, which are now often more accurate than a human, are one of the key functions driving new system-on-chip (SoC) investments to satisfy the development of AI for everyday applications. Using convolutional neural networks (CNNs) and other deep learning algorithms in vision applications have made such an impact that AI capabilities within SoCs are becoming pervasive. It was summarized effectively by Semico's 2018 AI Report "...some level of AI function in literally every type of silicon is strong and gaining momentum." In addition to vision, deep learning is used to solve complex problems such as 5G implementation for cellular infrastructure and simplifying 5G operational tasks through the capability to configure, optimize and repair itself, known as Self Organizing Networks (SON).
Google researchers developed a way to peer inside the minds of deep-learning systems, and the results are delightfully weird. What they did: The team built a tool that combines several techniques to provide people with a clearer idea of how neural networks make decisions. Applied to image classification, it lets a person visualize how the network develops its understanding of what is, for instance, a kitten or a Labrador. The visualizations, above, are ... strange. Why it matters: Deep learning is powerful--but opaque.
Voice AI is becoming increasingly ubiquitous and powerful. Forecasts suggest that voice commerce will be an $80 billion business by 2023. Google reports that 20% of their searches are made by voice query today -- a number that's predicted to climb to 50% by 2020. In 2017, Google announced that their speech recognition had a 95% accuracy rate. While that's an impressive number, it begs the question: 95% accurate for whom?
Many people who use a voice assistant, such as Alexa or Google Home, will be familiar with them not fulling understanding commands. But now it appears they may be worse at understanding women than men. Polling company YouGov asked 1000 people in the UK about voice assistants. Around two thirds of the female participants said the devices failed to respond their voice commands some of the time compared to half of the men. "Our research reveals that women are more likely to encounter problems being understood by a smart speaker than men, …
UBS Card Center, which processes roughly 25 percent of all credit cards in Switzerland, has won the Security Innovation of the Year award at the Retail Banker International Awards, presented in London. UBS Card Center's fraud team used the the latest artificial intelligence and machine learning capabilities in the FICO Falcon Platform to stop 84 percent more fraudulent transactions last year than in 2015. The need to optimise costs in the face of fierce competition meant UBS Card Center had to keep fraud write-offs to the very minimum. They were facing new fraud attack volumes but needed to uphold the highest standards for customer experience and satisfaction. This required the use of machine learning to minimize consumer interruptions while investigating more potential cases of fraud, all without adding staff.
Recent years have seen the rise of artificial intelligence (AI) adoption in the marketing and media industries. While often a buzzword for marketers to make their work sound more exciting, the real benefits to brands center on the use of machines to carry out deep learning and make humans' jobs easier. AI is certainly growing in notoriety, with up to 85% of UK businesses said to be set to invest in the field by 2020. In addition, studies have shown the gradual uptake of soft robotics in the home – 23-32% of households in the US and 18% of households in the UK have at least one voice assistant, the most popular models being either Amazon's Alexa or Google Assistant. Moreover, Apple claimed in 2018 that a staggering 500 million of its users now frequently make use of Siri, its pre-installed voice assistant.
Visiongain has launched a new cyber report Artificial Intelligence in Cyber Security Market Report 2019-2029: Forecasts by Component (Hardware, Software, Services), by Deployment Type (On-premise, Cloud, Hybrid), by Security Type (Endpoint Security, Network Security, Application Security, Cloud Security), by Technology (Machine Learning, Natural Language Processing, Context Awareness Computing), by Application (Antivirus/Antimalware, Identity and Access Management (IAM), Risk and Compliance Management, Intrusion Detection/Prevention Systems, Encryption, Unified Threat Management (UTM), Data Loss Prevention (DLP), Others), by Industry (Enterprise, BFSI, Government & Defence, Retail, Healthcare, Manufacturing, Automotive & Transportation, Others), Leading Company Analysis, Regional and Leading National Market Analysis. The increasing number of cyber frauds and malicious attacks is one of the prime growth factors of artificial intelligence in the cyber security market. In addition, the growing adoption of bring your own devices (BYOD) in organisations is also anticipated to drive the growth of artificial intelligence in the cyber security market. With the rising incidences of cyber-crimes, artificial intelligence in the cybersecurity market will gain traction in years to come. Cyber-frauds such as identity and payment card thefts, account for over 55% of all cyber-crimes and may prove costly for organisations, if not resolved quickly.