It's safe to say that artificial intelligence (AI) has established itself in the business world. Think of it as part of a company that is always working and doesn't take time off. The efficiencies AI presents are so tremendous that it is expected to result in more than $1 trillion in savings in the industry. As I write this, financial services firms are harnessing AI to provide new opportunities for enhancing call centers, detecting fraud, and enabling a smarter level of trading. The financial services market is filled with companies offering customers with products and services to expand their portfolios; therefore, firms that maximize their customer service efforts will succeed. Major firms that are trying to increase customer retention are implementing chatbots in their call centers to answer basic queries, give advice, direct calls, and provide an overall better customer experience.

Nvidia announced its new enterprise software product, vComputeServer, which has been developed and optimised for use with VMware's vSphere. Last week, VMware announced its intention to acquire Carbon Black and Pivotal, in a massive deal that will expand the company's SaaS offerings, while enhancing its ability to enable digital transformation for customers. Before the dust had even settled on that news, the company announced today (26 August), that it is set to launch a hybrid cloud on AWS (Amazon Web Services) in partnership with Nvidia, which will improve GPU (graphics processing unit) virtualisation. The two companies say that this is the first hybrid cloud service that lets enterprises accelerate AI, machine learning or deep learning workloads with GPUs. At the VMWorld conference in San Francisco, Nvidia's VP of product management, John Fanelli, told reporters: "In a modern data centre, organisations are going to be using GPUs to power AI, deep learning and analytics. "Due to the scale of those types of workloads, they're going to be doing some processing on premise in data centres, some processing in clouds and continually iterating between them." The company said that this will make the completion of deep learning training up to 50 times faster than with a CPU alone. This product is aimed at people who may be using Nvidia's Rapids software, Fanelli explained, which is a suite of data processing and machine learning libraries used for GPU-acceleration in data science workflows. Nvidia founder and CEO Jensen Huang said: "From operational intelligence to artificial intelligence, businesses rely on GPU-accelerated computing to make fast, accurate predictions that directly impact their bottom line.

IBM designed Summit, the fastest supercomputer on Earth, to run the calculation-intensive models that power modern artificial intelligence (AI). Now MIT is about to get a slice. IBM pledged earlier this year to donate an $11.6 million computer cluster to MIT modeled after the architecture of Summit, the supercomputer it built at Oak Ridge National Laboratory for the U.S. Department of Energy. The donated cluster is expected to come online this fall when the MIT Stephen A. Schwarzman College of Computing opens its doors, allowing researchers to run more elaborate AI models to tackle a range of problems, from developing a better hearing aid to designing a longer-lived lithium-ion battery. "We're excited to see a range of AI projects at MIT get a computing boost, and we can't wait to see what magic awaits," says John E. Kelly III, executive vice president of IBM, who announced the gift in February at MIT's launch celebration of the MIT Schwarzman College of Computing.

Many gamers around the world love to play classic games such as Elder Scrolls, Super Mario and Metal Gear but one thing about older games, like ones made in the 1990s, is that they can lack localisations for each region. As well as that, some games are released exclusively in specific regions, so Japanese exclusives will only be made in Japanese. An example of this is Mother 3 that was released only in Japan after the highly acclaimed Mother 2 (Earthbound) that had a worldwide release. This meant that the fans had to translate the Japanese text if they wanted to know what was going on. RetroArch is a popular open-source gaming emulator where you can play classic games from consoles like the Gamecube on your PC.

An electrocardiogram, also known as an EKG or ECG, is a test used to measure the electrical activity of the heart. While it's known that a patient's sex and age could affect an EKG, researchers hypothesized that artificial intelligence could determine a patient's gender and estimate their'physiologic age' -- a measure of overall body function and health status distinct from chronological age. Using EKG data of almost 500,000 patients, a type of artificial intelligence known as a convolutional neural network was trained to find similarities among the input and output data. Once trained, the neural network was tested for accuracy on the data of an additional 275,000 patients by predicting the output when only given input data. The neural network estimated a patient's chronological age as higher after experiencing adverse health situations such as heart attack, low ejection fraction and coronary artery disease, and lower age if they experienced few or no adverse events.

JERUSALEM – The long shadow war between Israel and Iran has burst into the open in recent days, with Israel allegedly striking Iran-linked targets as far away as Iraq and crash-landing two drones in Hezbollah-dominated southern Beirut. These incidents, along with an air raid in Syria that Israel says thwarted an imminent Iranian drone attack, have raised tensions at a particularly fraught time. Israeli Prime Minister Benjamin Netanyahu is looking to project strength three weeks before national elections, while Iran has taken a series of provocative actions in recent months aimed at pressuring European nations to provide relief from crippling U.S. sanctions. Hassan Nasrallah, leader of the Iran-backed Hezbollah, vowed to retaliate after a drone crashed on the militant group's Beirut media office and another exploded midair early Sunday. Israeli forces along the border with Lebanon are on high alert, raising fears of a repeat of the 2006 war.

SAN JOSE, CALIFORNIA – A former Google engineer was charged Tuesday with stealing closely guarded secrets that he later sold to Uber as the ride-hailing service scrambled to catch up in the high-stakes race to build robotic vehicles. The indictment filed by the U.S. Attorney's office in San Jose, California, is an offshoot of a lawsuit filed in 2017 by Waymo, a self-driving car pioneer spun off from Google. Uber agreed to pay Waymo $245 million to settle the case, but the federal judge overseeing the lawsuit made an unusual recommendation to open a criminal probe. Uber considered having self-driving technology crucial to survive. Anthony Levandowski, a pioneer in robotic vehicles, was charged with 33 counts of trade secrets theft.

As a pen tester, what do you do when trying to hack into the external web perimeter of a massive company when the scope entails over 100,000 domains and machines to inspect? All the low-hanging CVE fruit has long been picked clean by automated scanners, so there are no obvious ways in. Yet they exist, even if you don't know exactly what you're looking for. You know it when you see the kind of page that will still bear fruit: some old-looking custom web app that can be exploited, some administration page with a login that could be brute-forced, something "interesting." But how do you get to the "interesting" more quickly?

This review presents machine learning (ML) approaches from an applied economist's perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis. Machine learning (ML) offers great potential for expanding the applied economist's toolbox. ML tools are beginning to be employed in economic analysis (März et al., 2016; Crane-Droesch, 2017; Athey, 2019), while some researchers raise concerns about their transparency, interpretability and use for ...

Defense and intelligence have long been a leader when it comes to AI. The Department of Defense (DoD) recently launched the Joint AI Center (JAIC) with a mission to transform the DoD by accelerating the delivery and adoption of AI to achieve mission impact at scale. This center is meant to provide expertise to help the DOD harness the game-changing power of AI. A few people listed who are influential in helping continue drive AI investment and technologies forward in the DOD are LTG Jack Shanahan who is the JAIC Director. In this role General Shanahan is responsible for accelerating the delivery of AI-enabled capabilities, scaling the department-wide impact of AI and synchronizing AI activities to expand joint force advantages.