"We sleep safely at night because rough men stand ready to visit violence on those who would harm us." American military recruiting is in dire straits. The Government Accountability Office (GAO) recently determined the Defense Department confronts its most challenging recruitment environment in 50 years, reporting the department "does not have sufficient plans, goals, and strategies to guide its recruitment and retention efforts." Recruiters for the armed services face a daunting task, especially amid a hot job market and polarized cultural landscape. Existing AI tools have the potential to streamline and focus their efforts, pre-identifying those most qualified and most likely to want to serve.

Artificial Intelligence (AI) is expected to go mainstream in the APAC region by the end of 2023 with more than 88% of enterprises in the region already using or planning to use AI or machine learning (ML) applications over the next 12 months, according to an IDC report, jointly commissioned by Lenovo and AMD. "AI applications enable CIOs to analyze large volumes of information and create real-time insights to drive customer engagement and customer experience, managing growing complexity of a rapidly expanding geo-dispersed infrastructure for higher levels of resiliency and agility, and securing their IT operations against the backdrop of growing incidence of ransomware and malware attacks," said the IDC report, which was based on a study of over 900 CIOs & IT decision makers across Asia Pacific. The top three business processes in which enterprises are expected to incorporate AI/ML include IT operations, cybersecurity, and customer support and service, the study noted. "Organizations are seeking AI/ML to streamline IT operations, as till today there are organizations that take time to get a virtual machine allocated to a customer," said Amit Luthra, managing director for India at Lenovo ISG. Given that in the present business environment there are some workloads in the public cloud while some in the hybrid cloud, it becomes difficult to define the operations and ascertain who presides over what data, Luthra pointed out.

Implementing AI can introduce disruptive change and disfranchise staff and employees. When members are reluctant to adopt a new technology, they might hesitate to use it, push back against its deployment, or use it in limited capacity — which affects the benefits an organization gains from using it in the first place. Organizations often don’t see the problems coming, and rollout a new tool too quickly only for it to run into major barriers. To navigate this process, we propose a three-step approach: 1) assess the impact of an AI solution, 2) identify barriers to adoption, and 3) identify the appropriate pace.

After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-powered robots to run their warehouses--and even to automatically order stock when inventory runs low. Utilities use AI to forecast electricity demand. A confluence of developments is driving this new wave of AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI--data.

Artificial Intelligence (AI) can unlock huge opportunities for both enterprises and governments, but AI needs to be deployed at large scale for the same. According to Rama Devi Lanka, director, Emerging Technologies and officer on special duty at ITE&C, Government of Telangana, the government is looking to deploy AI at the same scale as India's Unified Payments Interface (UPI). "If you look at the unified payments interface (UPI), that is the scale at which we are looking to deploy AI," she added. Speaking at the Mint AI Summit on April 29, Lanka said that Telangana has identified agriculture, healthcare, education, transportation, smart cities, and climate change as some of the focus areas for AI implementation. However, she noted that the lack of availability of data can be a problem.

For years, there has been a buzz around AI and how it would revolutionize industrial IoT, changing our lives forever. Organizations would realize enormous gains in productivity, do much more with less, improve working conditions, and reduce waste. AI would usher in an age of reliable devices that do our bidding without issue. This begs the question, given all the potential gains in the industrial space and the oceans of data being created, where's the AI in the land of industrial IoT? The answer is not as complicated as you might think.

Humans invented artificial intelligence, so it is an unfortunate reality that human biases can be baked into AI. Businesses that use AI, however, do not need to replicate these historical mistakes. Today, we can deploy and scale carefully designed AI across organizations to root out bias rather than reinforce it. This shift is happening now in consumer lending, an industry with a history of using biased systems and processes to write loans. For years, creditors have used models that misrepresent the creditworthiness of women and minorities with discriminatory credit-scoring systems and other practices. Until recently, for example, consistently paying rent did not help on mortgage applications, an exclusion that especially disadvantaged people of color.

The pandemic is now the biggest and most critical challenge of traditional banking. Some of these challenges are revenue pressure, data security, customer service management, data collection and analysis, risk management, and so on. These are the warning lights and alarm bells that call for caution over emerging risks. AI (Artificial Intelligence) has gained recognition as an effective solution. AI is empowering the banking industry to provide individualized frictionless customer experiences.

After months in early release, Nvidia today announced the general availability of Nvidia AI Enterprise, a new software offering that's designed to bring AI capabilities to the masses via VMware's vSphere. The announcement also includes precertification of AI Enterprise running on a handful of industry-standard X64 servers (equipped with GPUs, of course), as well as a partnership with Domino Data Labs for MLOps. "AI is real and it has real value," said Manuvir Das, Nvidia's head of enterprise computing. Das knows it's real because Nvidia has helped thousands of customers deploy AI into their operations. However, AI has also proven to be difficult to implement, he said. "And the reason is because, on the one hand, it's an end-to-end problem, from the acquisition of data to the training to produce models and then deploy the models to production," Das said.

The pandemic that has raged across the globe over the past year has shone a cold, hard light on many things--the varied levels of preparedness to respond; collective attitudes toward health, technology, and science; and vast financial and social inequities. As the world continues to navigate the covid-19 health crisis, and some places even begin a gradual return to work, school, travel, and recreation, it's critical to resolve the competing priorities of protecting the public's health equitably while ensuring privacy. The extended crisis has led to rapid change in work and social behavior, as well as an increased reliance on technology. The expanded and rapid adoption of artificial intelligence (AI) demonstrates how adaptive technologies are prone to intersect with humans and social institutions in potentially risky or inequitable ways. "Our relationship with technology as a whole will have shifted dramatically post-pandemic," says Yoav Schlesinger, principal of the ethical AI practice at Salesforce.